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Scientific Research – Types, Purpose and Guide

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Scientific Research

Definition:

Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge.

It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses. Scientific research can be conducted in various fields, such as natural sciences, social sciences, and engineering, and may involve experiments, observations, surveys, or other forms of data collection. The goal of scientific research is to advance knowledge, improve understanding, and contribute to the development of solutions to practical problems.

Types of Scientific Research

There are different types of scientific research, which can be classified based on their purpose, method, and application. In this response, we will discuss the four main types of scientific research.

Descriptive Research

Descriptive research aims to describe or document a particular phenomenon or situation, without altering it in any way. This type of research is usually done through observation, surveys, or case studies. Descriptive research is useful in generating ideas, understanding complex phenomena, and providing a foundation for future research. However, it does not provide explanations or causal relationships between variables.

Exploratory Research

Exploratory research aims to explore a new area of inquiry or develop initial ideas for future research. This type of research is usually conducted through observation, interviews, or focus groups. Exploratory research is useful in generating hypotheses, identifying research questions, and determining the feasibility of a larger study. However, it does not provide conclusive evidence or establish cause-and-effect relationships.

Experimental Research

Experimental research aims to test cause-and-effect relationships between variables by manipulating one variable and observing the effects on another variable. This type of research involves the use of an experimental group, which receives a treatment, and a control group, which does not receive the treatment. Experimental research is useful in establishing causal relationships, replicating results, and controlling extraneous variables. However, it may not be feasible or ethical to manipulate certain variables in some contexts.

Correlational Research

Correlational research aims to examine the relationship between two or more variables without manipulating them. This type of research involves the use of statistical techniques to determine the strength and direction of the relationship between variables. Correlational research is useful in identifying patterns, predicting outcomes, and testing theories. However, it does not establish causation or control for confounding variables.

Scientific Research Methods

Scientific research methods are used in scientific research to investigate phenomena, acquire knowledge, and answer questions using empirical evidence. Here are some commonly used scientific research methods:

Observational Studies

This method involves observing and recording phenomena as they occur in their natural setting. It can be done through direct observation or by using tools such as cameras, microscopes, or sensors.

Experimental Studies

This method involves manipulating one or more variables to determine the effect on the outcome. This type of study is often used to establish cause-and-effect relationships.

Survey Research

This method involves collecting data from a large number of people by asking them a set of standardized questions. Surveys can be conducted in person, over the phone, or online.

Case Studies

This method involves in-depth analysis of a single individual, group, or organization. Case studies are often used to gain insights into complex or unusual phenomena.

Meta-analysis

This method involves combining data from multiple studies to arrive at a more reliable conclusion. This technique can be used to identify patterns and trends across a large number of studies.

Qualitative Research

This method involves collecting and analyzing non-numerical data, such as interviews, focus groups, or observations. This type of research is often used to explore complex phenomena and to gain an understanding of people’s experiences and perspectives.

Quantitative Research

This method involves collecting and analyzing numerical data using statistical techniques. This type of research is often used to test hypotheses and to establish cause-and-effect relationships.

Longitudinal Studies

This method involves following a group of individuals over a period of time to observe changes and to identify patterns and trends. This type of study can be used to investigate the long-term effects of a particular intervention or exposure.

Data Analysis Methods

There are many different data analysis methods used in scientific research, and the choice of method depends on the type of data being collected and the research question. Here are some commonly used data analysis methods:

• Descriptive statistics: This involves using summary statistics such as mean, median, mode, standard deviation, and range to describe the basic features of the data.
• Inferential statistics: This involves using statistical tests to make inferences about a population based on a sample of data. Examples of inferential statistics include t-tests, ANOVA, and regression analysis.
• Qualitative analysis: This involves analyzing non-numerical data such as interviews, focus groups, and observations. Qualitative analysis may involve identifying themes, patterns, or categories in the data.
• Content analysis: This involves analyzing the content of written or visual materials such as articles, speeches, or images. Content analysis may involve identifying themes, patterns, or categories in the content.
• Data mining: This involves using automated methods to analyze large datasets to identify patterns, trends, or relationships in the data.
• Machine learning: This involves using algorithms to analyze data and make predictions or classifications based on the patterns identified in the data.

Application of Scientific Research

Scientific research has numerous applications in many fields, including:

• Medicine and healthcare: Scientific research is used to develop new drugs, medical treatments, and vaccines. It is also used to understand the causes and risk factors of diseases, as well as to develop new diagnostic tools and medical devices.
• Agriculture : Scientific research is used to develop new crop varieties, to improve crop yields, and to develop more sustainable farming practices.
• Technology and engineering : Scientific research is used to develop new technologies and engineering solutions, such as renewable energy systems, new materials, and advanced manufacturing techniques.
• Environmental science : Scientific research is used to understand the impacts of human activity on the environment and to develop solutions for mitigating those impacts. It is also used to monitor and manage natural resources, such as water and air quality.
• Education : Scientific research is used to develop new teaching methods and educational materials, as well as to understand how people learn and develop.
• Business and economics: Scientific research is used to understand consumer behavior, to develop new products and services, and to analyze economic trends and policies.
• Social sciences : Scientific research is used to understand human behavior, attitudes, and social dynamics. It is also used to develop interventions to improve social welfare and to inform public policy.

How to Conduct Scientific Research

Conducting scientific research involves several steps, including:

• Identify a research question: Start by identifying a question or problem that you want to investigate. This question should be clear, specific, and relevant to your field of study.
• Conduct a literature review: Before starting your research, conduct a thorough review of existing research in your field. This will help you identify gaps in knowledge and develop hypotheses or research questions.
• Develop a research plan: Once you have a research question, develop a plan for how you will collect and analyze data to answer that question. This plan should include a detailed methodology, a timeline, and a budget.
• Collect data: Depending on your research question and methodology, you may collect data through surveys, experiments, observations, or other methods.
• Analyze data: Once you have collected your data, analyze it using appropriate statistical or qualitative methods. This will help you draw conclusions about your research question.
• Interpret results: Based on your analysis, interpret your results and draw conclusions about your research question. Discuss any limitations or implications of your findings.
• Communicate results: Finally, communicate your findings to others in your field through presentations, publications, or other means.

Purpose of Scientific Research

The purpose of scientific research is to systematically investigate phenomena, acquire new knowledge, and advance our understanding of the world around us. Scientific research has several key goals, including:

• Exploring the unknown: Scientific research is often driven by curiosity and the desire to explore uncharted territory. Scientists investigate phenomena that are not well understood, in order to discover new insights and develop new theories.
• Testing hypotheses: Scientific research involves developing hypotheses or research questions, and then testing them through observation and experimentation. This allows scientists to evaluate the validity of their ideas and refine their understanding of the phenomena they are studying.
• Solving problems: Scientific research is often motivated by the desire to solve practical problems or address real-world challenges. For example, researchers may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Advancing knowledge: Scientific research is a collective effort to advance our understanding of the world around us. By building on existing knowledge and developing new insights, scientists contribute to a growing body of knowledge that can be used to inform decision-making, solve problems, and improve our lives.

Examples of Scientific Research

Here are some examples of scientific research that are currently ongoing or have recently been completed:

• Clinical trials for new treatments: Scientific research in the medical field often involves clinical trials to test new treatments for diseases and conditions. For example, clinical trials may be conducted to evaluate the safety and efficacy of new drugs or medical devices.
• Genomics research: Scientists are conducting research to better understand the human genome and its role in health and disease. This includes research on genetic mutations that can cause diseases such as cancer, as well as the development of personalized medicine based on an individual’s genetic makeup.
• Climate change: Scientific research is being conducted to understand the causes and impacts of climate change, as well as to develop solutions for mitigating its effects. This includes research on renewable energy technologies, carbon capture and storage, and sustainable land use practices.
• Neuroscience : Scientists are conducting research to understand the workings of the brain and the nervous system, with the goal of developing new treatments for neurological disorders such as Alzheimer’s disease and Parkinson’s disease.
• Artificial intelligence: Researchers are working to develop new algorithms and technologies to improve the capabilities of artificial intelligence systems. This includes research on machine learning, computer vision, and natural language processing.
• Space exploration: Scientific research is being conducted to explore the cosmos and learn more about the origins of the universe. This includes research on exoplanets, black holes, and the search for extraterrestrial life.

When to use Scientific Research

Some specific situations where scientific research may be particularly useful include:

• Solving problems: Scientific research can be used to investigate practical problems or address real-world challenges. For example, scientists may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Decision-making: Scientific research can provide evidence-based information to inform decision-making. For example, policymakers may use scientific research to evaluate the effectiveness of different policy options or to make decisions about public health and safety.
• Innovation : Scientific research can be used to develop new technologies, products, and processes. For example, research on materials science can lead to the development of new materials with unique properties that can be used in a range of applications.
• Knowledge creation : Scientific research is an important way of generating new knowledge and advancing our understanding of the world around us. This can lead to new theories, insights, and discoveries that can benefit society.

Advantages of Scientific Research

There are many advantages of scientific research, including:

• Improved understanding : Scientific research allows us to gain a deeper understanding of the world around us, from the smallest subatomic particles to the largest celestial bodies.
• Evidence-based decision making: Scientific research provides evidence-based information that can inform decision-making in many fields, from public policy to medicine.
• Technological advancements: Scientific research drives technological advancements in fields such as medicine, engineering, and materials science. These advancements can improve quality of life, increase efficiency, and reduce costs.
• New discoveries: Scientific research can lead to new discoveries and breakthroughs that can advance our knowledge in many fields. These discoveries can lead to new theories, technologies, and products.
• Economic benefits : Scientific research can stimulate economic growth by creating new industries and jobs, and by generating new technologies and products.
• Improved health outcomes: Scientific research can lead to the development of new medical treatments and technologies that can improve health outcomes and quality of life for people around the world.
• Increased innovation: Scientific research encourages innovation by promoting collaboration, creativity, and curiosity. This can lead to new and unexpected discoveries that can benefit society.

Limitations of Scientific Research

Scientific research has some limitations that researchers should be aware of. These limitations can include:

• Research design limitations : The design of a research study can impact the reliability and validity of the results. Poorly designed studies can lead to inaccurate or inconclusive results. Researchers must carefully consider the study design to ensure that it is appropriate for the research question and the population being studied.
• Sample size limitations: The size of the sample being studied can impact the generalizability of the results. Small sample sizes may not be representative of the larger population, and may lead to incorrect conclusions.
• Time and resource limitations: Scientific research can be costly and time-consuming. Researchers may not have the resources necessary to conduct a large-scale study, or may not have sufficient time to complete a study with appropriate controls and analysis.
• Ethical limitations : Certain types of research may raise ethical concerns, such as studies involving human or animal subjects. Ethical concerns may limit the scope of the research that can be conducted, or require additional protocols and procedures to ensure the safety and well-being of participants.
• Limitations of technology: Technology may limit the types of research that can be conducted, or the accuracy of the data collected. For example, certain types of research may require advanced technology that is not yet available, or may be limited by the accuracy of current measurement tools.
• Limitations of existing knowledge: Existing knowledge may limit the types of research that can be conducted. For example, if there is limited knowledge in a particular field, it may be difficult to design a study that can provide meaningful results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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1 Science and scientific research

What is research? Depending on who you ask, you will likely get very different answers to this seemingly innocuous question. Some people will say that they routinely research different online websites to find the best place to buy the goods or services they want. Television news channels supposedly conduct research in the form of viewer polls on topics of public interest such as forthcoming elections or government-funded projects. Undergraduate students research on the Internet to find the information they need to complete assigned projects or term papers. Postgraduate students working on research projects for a professor may see research as collecting or analysing data related to their project. Businesses and consultants research different potential solutions to remedy organisational problems such as a supply chain bottleneck or to identify customer purchase patterns. However, none of the above can be considered ‘scientific research’ unless: it contributes to a body of science, and it follows the scientific method. This chapter will examine what these terms mean.

What is science? To some, science refers to difficult high school or university-level courses such as physics, chemistry, and biology meant only for the brightest students. To others, science is a craft practiced by scientists in white coats using specialised equipment in their laboratories. Etymologically, the word ‘science’ is derived from the Latin word scientia meaning knowledge. Science refers to a systematic and organised body of knowledge in any area of inquiry that is acquired using ‘the scientific method’ (the scientific method is described further below). Science can be grouped into two broad categories: natural science and social science. Natural science is the science of naturally occurring objects or phenomena, such as light, objects, matter, earth, celestial bodies, or the human body. Natural sciences can be further classified into physical sciences, earth sciences, life sciences, and others. Physical sciences consist of disciplines such as physics (the science of physical objects), chemistry (the science of matter), and astronomy (the science of celestial objects). Earth sciences consist of disciplines such as geology (the science of the earth). Life sciences include disciplines such as biology (the science of human bodies) and botany (the science of plants). In contrast, social science is the science of people or collections of people, such as groups, firms, societies, or economies, and their individual or collective behaviours. Social sciences can be classified into disciplines such as psychology (the science of human behaviours), sociology (the science of social groups), and economics (the science of firms, markets, and economies).

The natural sciences are different from the social sciences in several respects. The natural sciences are very precise, accurate, deterministic, and independent of the person making the scientific observations. For instance, a scientific experiment in physics, such as measuring the speed of sound through a certain media or the refractive index of water, should always yield the exact same results, irrespective of the time or place of the experiment, or the person conducting the experiment. If two students conducting the same physics experiment obtain two different values of these physical properties, then it generally means that one or both of those students must be in error. However, the same cannot be said for the social sciences, which tend to be less accurate, deterministic, or unambiguous. For instance, if you measure a person’s happiness using a hypothetical instrument, you may find that the same person is more happy or less happy (or sad) on different days and sometimes, at different times on the same day. One’s happiness may vary depending on the news that person received that day or on the events that transpired earlier during that day. Furthermore, there is not a single instrument or metric that can accurately measure a person’s happiness. Hence, one instrument may calibrate a person as being ‘more happy’ while a second instrument may find that the same person is ‘less happy’ at the same instant in time. In other words, there is a high degree of measurement error in the social sciences and there is considerable uncertainty and little agreement on social science policy decisions. For instance, you will not find many disagreements among natural scientists on the speed of light or the speed of the earth around the sun, but you will find numerous disagreements among social scientists on how to solve a social problem such as reduce global terrorism or rescue an economy from a recession. Any student studying the social sciences must be cognisant of and comfortable with handling higher levels of ambiguity, uncertainty, and error that come with such sciences, which merely reflects the high variability of social objects.

Sciences can also be classified based on their purpose. Basic sciences , also called pure sciences, are those that explain the most basic objects and forces, relationships between them, and laws governing them. Examples include physics, mathematics, and biology. Applied sciences , also called practical sciences, are sciences that apply scientific knowledge from basic sciences in a physical environment. For instance, engineering is an applied science that applies the laws of physics and chemistry for practical applications such as building stronger bridges or fuel efficient combustion engines, while medicine is an applied science that applies the laws of biology to solving human ailments. Both basic and applied sciences are required for human development. However, applied science cannot stand on its own right, but instead relies on basic sciences for its progress. Of course, industry and private enterprises tend to focus more on applied sciences given their practical value, while universities study both basic and applied sciences.

Scientific knowledge

The purpose of science is to create scientific knowledge. Scientific knowledge refers to a generalised body of laws and theories for explaining a phenomenon or behaviour of interest that is acquired using the scientific method. Laws are observed patterns of phenomena or behaviours, while theories are systematic explanations of the underlying phenomenon or behaviour. For instance, in physics, the Newtonian Laws of Motion describe what happens when an object is in a state of rest or motion (Newton’s First Law), what force is needed to move a stationary object or stop a moving object (Newton’s Second Law), and what happens when two objects collide (Newton’s Third Law). Collectively, the three laws constitute the basis of classical mechanics—a theory of moving objects. Likewise, the theory of optics explains the properties of light and how it behaves in different media, electromagnetic theory explains the properties of electricity and how to generate it, quantum mechanics explains the properties of subatomic particles, and thermodynamics explains the properties of energy and mechanical work. An introductory university level textbook in physics will likely contain separate chapters devoted to each of these theories. Similar theories are also available in social sciences. For instance, cognitive dissonance theory in psychology explains how people react when their observations of an event are different from what they expected of that event, general deterrence theory explains why some people engage in improper or criminal behaviours, such as to illegally download music or commit software piracy, and the theory of planned behaviour explains how people make conscious reasoned choices in their everyday lives.

The goal of scientific research is to discover laws and postulate theories that can explain natural or social phenomena, or in other words, build scientific knowledge. It is important to understand that this knowledge may be imperfect or even quite far from the truth. Sometimes, there may not be a single universal truth, but rather an equilibrium of ‘multiple truths.’ We must understand that the theories upon which scientific knowledge is based are only explanations of a particular phenomenon as suggested by a scientist. As such, there may be good or poor explanations depending on the extent to which those explanations fit well with reality, and consequently, there may be good or poor theories. The progress of science is marked by our progression over time from poorer theories to better theories, through better observations using more accurate instruments and more informed logical reasoning.

We arrive at scientific laws or theories through a process of logic and evidence. Logic (theory) and evidence (observations) are the two, and only two, pillars upon which scientific knowledge is based. In science, theories and observations are inter-related and cannot exist without each other. Theories provide meaning and significance to what we observe, and observations help validate or refine existing theory or construct new theory. Any other means of knowledge acquisition, such as faith or authority cannot be considered science.

Scientific research

Given that theories and observations are the two pillars of science, scientific research operates at two levels: a theoretical level and an empirical level. The theoretical level is concerned with developing abstract concepts about a natural or social phenomenon and relationships between those concepts (i.e., build ‘theories’), while the empirical level is concerned with testing the theoretical concepts and relationships to see how well they reflect our observations of reality, with the goal of ultimately building better theories. Over time, a theory becomes more and more refined (i.e., fits the observed reality better), and the science gains maturity. Scientific research involves continually moving back and forth between theory and observations. Both theory and observations are essential components of scientific research. For instance, relying solely on observations for making inferences and ignoring theory is not considered valid scientific research.

Depending on a researcher’s training and interest, scientific inquiry may take one of two possible forms: inductive or deductive. In inductive research , the goal of a researcher is to infer theoretical concepts and patterns from observed data. In deductive research , the goal of the researcher is to test concepts and patterns known from theory using new empirical data. Hence, inductive research is also called theory-building research, and deductive research is theory-testing research. Note here that the goal of theory testing is not just to test a theory, but possibly to refine, improve, and extend it. Figure 1.1 depicts the complementary nature of inductive and deductive research. Note that inductive and deductive research are two halves of the research cycle that constantly iterates between theory and observations. You cannot do inductive or deductive research if you are not familiar with both the theory and data components of research. Naturally, a complete researcher is one who can traverse the entire research cycle and can handle both inductive and deductive research.

It is important to understand that theory-building (inductive research) and theory-testing (deductive research) are both critical for the advancement of science. Elegant theories are not valuable if they do not match with reality. Likewise, mountains of data are also useless until they can contribute to the construction of meaningful theories. Rather than viewing these two processes in a circular relationship, as shown in Figure 1.1, perhaps they can be better viewed as a helix, with each iteration between theory and data contributing to better explanations of the phenomenon of interest and better theories. Though both inductive and deductive research are important for the advancement of science, it appears that inductive (theory-building) research is more valuable when there are few prior theories or explanations, while deductive (theory-testing) research is more productive when there are many competing theories of the same phenomenon and researchers are interested in knowing which theory works best and under what circumstances.

Theory building and theory testing are particularly difficult in the social sciences, given the imprecise nature of the theoretical concepts, inadequate tools to measure them, and the presence of many unaccounted for factors that can also influence the phenomenon of interest. It is also very difficult to refute theories that do not work. For instance, Karl Marx’s theory of communism as an effective means of economic production withstood for decades, before it was finally discredited as being inferior to capitalism in promoting economic growth and social welfare. Erstwhile communist economies like the Soviet Union and China eventually moved toward more capitalistic economies characterised by profit-maximising private enterprises. However, the recent collapse of the mortgage and financial industries in the United States demonstrates that capitalism also has its flaws and is not as effective in fostering economic growth and social welfare as previously presumed. Unlike theories in the natural sciences, social science theories are rarely perfect, which provides numerous opportunities for researchers to improve those theories or build their own alternative theories.

Conducting scientific research, therefore, requires two sets of skills—theoretical and methodological—needed to operate in the theoretical and empirical levels respectively. Methodological skills (‘know-how’) are relatively standard, invariant across disciplines, and easily acquired through doctoral programs. However, theoretical skills (‘know-what’) are considerably harder to master, require years of observation and reflection, and are tacit skills that cannot be ‘taught’ but rather learned though experience. All of the greatest scientists in the history of mankind, such as Galileo, Newton, Einstein, Niels Bohr, Adam Smith, Charles Darwin, and Herbert Simon, were master theoreticians, and they are remembered for the theories they postulated that transformed the course of science. Methodological skills are needed to be an ordinary researcher, but theoretical skills are needed to be an extraordinary researcher!

Scientific method

In the preceding sections, we described science as knowledge acquired through a scientific method. So what exactly is the ‘scientific method’? Scientific method refers to a standardised set of techniques for building scientific knowledge, such as how to make valid observations, how to interpret results, and how to generalise those results. The scientific method allows researchers to independently and impartially test pre-existing theories and prior findings, and subject them to open debate, modifications, or enhancements. The scientific method must satisfy four key characteristics:

Replicability : Others should be able to independently replicate or repeat a scientific study and obtain similar, if not identical, results. Precision : Theoretical concepts, which are often hard to measure, must be defined with such precision that others can use those definitions to measure those concepts and test that theory. Falsifiability : A theory must be stated in such a way that it can be disproven. Theories that cannot be tested or falsified are not scientific theories and any such knowledge is not scientific knowledge. A theory that is specified in imprecise terms or whose concepts are not accurately measureable cannot be tested, and is therefore not scientific. Sigmund Freud’s ideas on psychoanalysis fall into this category and are therefore not considered a ‘theory’, even though psychoanalysis may have practical utility in treating certain types of ailments. Parsimony: When there are multiple different explanations of a phenomenon, scientists must always accept the simplest or logically most economical explanation. This concept is called parsimony or ‘Occam’s razor’. Parsimony prevents scientists from pursuing overly complex or outlandish theories with an endless number of concepts and relationships that may explain a little bit of everything but nothing in particular. Any branch of inquiry that does not allow the scientific method to test its basic laws or theories cannot be called ‘science’. For instance, theology (the study of religion) is not science because theological ideas—such as the presence of God—cannot be tested by independent observers using a logical, confirmable, repeatable, and scrutinisable. Similarly, arts, music, literature, humanities, and law are also not considered science, even though they are creative and worthwhile endeavours in their own right.

The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques for collecting and analysing qualitative or quantitative data. These methods include laboratory experiments, field surveys, case research, ethnographic research, action research, and so forth. Much of this book is devoted to learning about these different methods. However, recognise that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyse these observations. Very little of this method is directly pertinent to the theoretical level, which is really the more challenging part of scientific research.

Types of scientific research

Depending on the purpose of research, scientific research projects can be grouped into three types: exploratory, descriptive, and explanatory. Exploratory research is often conducted in new areas of inquiry, where the goals of the research are: to scope out the magnitude or extent of a particular phenomenon, problem, or behaviour, to generate some initial ideas (or ‘hunches’) about that phenomenon, or to test the feasibility of undertaking a more extensive study regarding that phenomenon. For instance, if the citizens of a country are generally dissatisfied with governmental policies during an economic recession, exploratory research may be directed at measuring the extent of citizens’ dissatisfaction, understanding how such dissatisfaction is manifested, such as the frequency of public protests, and the presumed causes of such dissatisfaction, such as ineffective government policies in dealing with inflation, interest rates, unemployment, or higher taxes. Such research may include examination of publicly reported figures, such as estimates of economic indicators, such as gross domestic product (GDP), unemployment, and consumer price index (CPI), as archived by third-party sources, obtained through interviews of experts, eminent economists, or key government officials, and/or derived from studying historical examples of dealing with similar problems. This research may not lead to a very accurate understanding of the target problem, but may be worthwhile in scoping out the nature and extent of the problem and serve as a useful precursor to more in-depth research.

Descriptive research is directed at making careful observations and detailed documentation of a phenomenon of interest. These observations must be based on the scientific method (i.e., must be replicable, precise, etc.), and therefore, are more reliable than casual observations by untrained people. Examples of descriptive research are tabulation of demographic statistics by the United States Census Bureau or employment statistics by the Bureau of Labor, who use the same or similar instruments for estimating employment by sector or population growth by ethnicity over multiple employment surveys or censuses. If any changes are made to the measuring instruments, estimates are provided with and without the changed instrumentation to allow the readers to make a fair before-and-after comparison regarding population or employment trends. Other descriptive research may include chronicling ethnographic reports of gang activities among adolescent youth in urban populations, the persistence or evolution of religious, cultural, or ethnic practices in select communities, and the role of technologies such as Twitter and instant messaging in the spread of democracy movements in Middle Eastern countries.

Explanatory research seeks explanations of observed phenomena, problems, or behaviours. While descriptive research examines the what, where, and when of a phenomenon, explanatory research seeks answers to questions of why and how. It attempts to ‘connect the dots’ in research, by identifying causal factors and outcomes of the target phenomenon. Examples include understanding the reasons behind adolescent crime or gang violence, with the goal of prescribing strategies to overcome such societal ailments. Most academic or doctoral research belongs to the explanation category, though some amount of exploratory and/or descriptive research may also be needed during initial phases of academic research. Seeking explanations for observed events requires strong theoretical and interpretation skills, along with intuition, insights, and personal experience. Those who can do it well are also the most prized scientists in their disciplines.

History of scientific thought

Before closing this chapter, it may be interesting to go back in history and see how science has evolved over time and identify the key scientific minds in this evolution. Although instances of scientific progress have been documented over many centuries, the terms ‘science’, ’scientists’, and the ‘scientific method’ were coined only in the nineteenth century. Prior to this time, science was viewed as a part of philosophy, and coexisted with other branches of philosophy such as logic, metaphysics, ethics, and aesthetics, although the boundaries between some of these branches were blurred.

In the earliest days of human inquiry, knowledge was usually recognised in terms of theological precepts based on faith. This was challenged by Greek philosophers such as Plato, Aristotle, and Socrates during the third century BC, who suggested that the fundamental nature of being and the world can be understood more accurately through a process of systematic logical reasoning called rationalism . In particular, Aristotle’s classic work Metaphysics (literally meaning ‘beyond physical [existence]’) separated theology (the study of Gods) from ontology (the study of being and existence) and universal science (the study of first principles, upon which logic is based). Rationalism (not to be confused with ‘rationality’) views reason as the source of knowledge or justification, and suggests that the criterion of truth is not sensory but rather intellectual and deductive, often derived from a set of first principles or axioms (such as Aristotle’s ‘law of non-contradiction’).

The next major shift in scientific thought occurred during the sixteenth century, when British philosopher Francis Bacon (1561–1626) suggested that knowledge can only be derived from observations in the real world. Based on this premise, Bacon emphasised knowledge acquisition as an empirical activity (rather than as a reasoning activity), and developed empiricism as an influential branch of philosophy. Bacon’s works led to the popularisation of inductive methods of scientific inquiry, the development of the ‘scientific method’ (originally called the ‘Baconian method’), consisting of systematic observation, measurement, and experimentation, and may have even sowed the seeds of atheism or the rejection of theological precepts as ‘unobservable’.

Empiricism continued to clash with rationalism throughout the Middle Ages, as philosophers sought the most effective way of gaining valid knowledge. French philosopher Rene Descartes sided with the rationalists, while British philosophers John Locke and David Hume sided with the empiricists. Other scientists, such as Galileo Galilei and Sir Isaac Newton, attempted to fuse the two ideas into natural philosophy (the philosophy of nature), to focus specifically on understanding nature and the physical universe, which is considered to be the precursor of the natural sciences. Galileo (1564–1642) was perhaps the first to state that the laws of nature are mathematical, and contributed to the field of astronomy through an innovative combination of experimentation and mathematics.

In the eighteenth century, German philosopher Immanuel Kant sought to resolve the dispute between empiricism and rationalism in his book Critique of pure r eason by arguing that experiences are purely subjective and processing them using pure reason without first delving into the subjective nature of experiences will lead to theoretical illusions. Kant’s ideas led to the development of German idealism , which inspired later development of interpretive techniques such as phenomenology, hermeneutics, and critical social theory.

At about the same time, French philosopher Auguste Comte (1798–1857), founder of the discipline of sociology, attempted to blend rationalism and empiricism in a new doctrine called positivism . He suggested that theory and observations have circular dependence on each other. While theories may be created via reasoning, they are only authentic if they can be verified through observations. The emphasis on verification started the separation of modern science from philosophy and metaphysics and further development of the ‘scientific method’ as the primary means of validating scientific claims. Comte’s ideas were expanded by Emile Durkheim in his development of sociological positivism (positivism as a foundation for social research) and Ludwig Wittgenstein in logical positivism.

In the early twentieth century, strong accounts of positivism were rejected by interpretive sociologists (antipositivists) belonging to the German idealism school of thought. Positivism was typically equated with quantitative research methods such as experiments and surveys and without any explicit philosophical commitments, while antipositivism employed qualitative methods such as unstructured interviews and participant observation. Even practitioners of positivism, such as American sociologist Paul Lazarsfield who pioneered large-scale survey research and statistical techniques for analysing survey data, acknowledged potential problems of observer bias and structural limitations in positivist inquiry. In response, antipositivists emphasised that social actions must be studied though interpretive means based upon understanding the meaning and purpose that individuals attach to their personal actions, which inspired Georg Simmel’s work on symbolic interactionism, Max Weber’s work on ideal types, and Edmund Husserl’s work on phenomenology.

In the mid-to-late twentieth century, both positivist and antipositivist schools of thought were subjected to criticisms and modifications. British philosopher Sir Karl Popper suggested that human knowledge is based not on unchallengeable, rock solid foundations, but rather on a set of tentative conjectures that can never be proven conclusively, but only disproven. Empirical evidence is the basis for disproving these conjectures or ‘theories’. This metatheoretical stance, called postpositivism (or postempiricism), amends positivism by suggesting that it is impossible to verify the truth although it is possible to reject false beliefs, though it retains the positivist notion of an objective truth and its emphasis on the scientific method.

Likewise, antipositivists have also been criticised for trying only to understand society but not critiquing and changing society for the better. The roots of this thought lie in Das k apital , written by German philosophers Karl Marx and Friedrich Engels, which critiqued capitalistic societies as being socially inequitable and inefficient, and recommended resolving this inequity through class conflict and proletarian revolutions. Marxism inspired social revolutions in countries such as Germany, Italy, Russia, and China, but generally failed to accomplish the social equality that it aspired. Critical research (also called critical theory) propounded by Max Horkheimer and Jürgen Habermas in the twentieth century, retains similar ideas of critiquing and resolving social inequality, and adds that people can and should consciously act to change their social and economic circumstances, although their ability to do so is constrained by various forms of social, cultural and political domination. Critical research attempts to uncover and critique the restrictive and alienating conditions of the status quo by analysing the oppositions, conflicts and contradictions in contemporary society, and seeks to eliminate the causes of alienation and domination (i.e., emancipate the oppressed class). More on these different research philosophies and approaches will be covered in future chapters of this book.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Conduct Scientific Research?

United Nations Educational, Scientific and Cultural Organization (UNESCO) defines research as systematic and creative actions taken to increase knowledge about humans, culture, and society and to apply it in new areas of interest. Scientific research is the research performed by applying systematic and constructed scientific methods to obtain, analyze, and interpret data.

Scientific research is the neutral, systematic, planned, and multiple-step process that uses previously discovered facts to advance knowledge that does not exist in the literature. It can be classified as observational or experimental with respect to data collection techniques, descriptive or analytical with respect to causality, and prospective, retrospective, or cross-sectional with respect to time ( 1 ).

All scientific investigations start with a specific research question and the formulation of a hypothesis to answer this question. Hypothesis should be clear, specific, and directly aim to answer the research question. A strong and testable hypothesis is the fundamental part of the scientific research. The next step is testing the hypothesis using scientific method to approve or disapprove it.

Scientific method should be neutral, objective, rational, and as a result, should be able to approve or disapprove the hypothesis. The research plan should include the procedure to obtain data and evaluate the variables. It should ensure that analyzable data are obtained. It should also include plans on the statistical analysis to be performed. The number of subjects and controls needed to get valid statistical results should be calculated, and data should be obtained in appropriate numbers and methods. The researcher should be continuously observing and recording all data obtained.

Data should be analyzed with the most appropriate statistical methods and be rearranged to make more sense if needed. Unfortunately, results obtained via analyses are not always sufficiently clear. Multiple reevaluations of data, review of the literature, and interpretation of results in light of previous research are required. Only after the completion of these stages can a research be written and presented to the scientific society. A well-conducted and precisely written research should always be open to scientific criticism. It should also be kept in mind that research should be in line with ethical rules all through its stages.

Actually, psychiatric research has been developing rapidly, possibly even more than any other medical field, thus reflecting the utilization of new research methods and advanced treatment technologies. Nevertheless, basic research principles and ethical considerations keep their importance.

Ethics are standards used to differentiate acceptable and unacceptable behavior. Adhering to ethical standards in scientific research is noteworthy because of many different reasons. First, these standards promote the aims of research, such as knowledge, truth, and avoidance of error. For example, prohibitions against fabricating, falsifying, or misrepresenting research data promote truth and minimize error. In addition, ethical standards promote values that are essential to collaborative work, such as trust, accountability, mutual respect, and fairness. Many ethical standards in research, such as guidelines for authorship, copyright and patenting policies, data-sharing policies, and confidentiality rules in peer review, are designed to protect intellectual property interests while encouraging collaboration. Many ethical standards such as policies on research misconduct and conflicts of interest are necessary to ensure that researchers can be held accountable to the public. Last but not the least, ethical standards of research promote a variety of other important moral and social values, such as social responsibility, human rights, animal welfare, compliance with the law, and public health and safety ( 2 ). In conclusion, for the good of science and humanity, research has the inevitable responsibility of precisely transferring the knowledge to new generations ( 3 ).

In medical research, all clinical investigations are obliged to comply with some ethical principles. These principles could be summarized as respect to humans, respect to the society, benefit, harmlessness, autonomy, and justice. Respect to humans indicates that all humans have the right to refuse to participate in an investigation or to withdraw their consent any time without any repercussions. Respect to society indicates that clinical research should seek answers to scientific questions using scientific methods and should benefit the society. Benefit indicates that research outcomes are supposed to provide solutions to a health problem. Harmlessness describes all necessary precautions that are taken to protect volunteers from potential harm. Autonomy indicates that participating in research is voluntary and with freewill. Justice indicates that subject selection is based on justice and special care is taken for special groups that could be easily traumatized ( 4 ).

In psychiatric studies, if the patient is not capable of giving consent, the relatives have the right to consent on behalf of the patient. This is based on the idea of providing benefit to the patient with discovery of new treatment methods via research. However, the relatives’ consent rights are under debate from an ethical point of view. On the other hand, research on those patients aim to directly get new knowledge about them, and it looks like an inevitable necessity. The only precaution that could be taken to overcome this ambivalence has been the scrupulous audit of the Research Ethic Committees. Still, there are many examples that show that this method is not always able to prevent patient abuse ( 5 ). Therefore, it is difficult to claim autonomy when psychiatric patients are studied, and psychiatric patients are considered among patients to require special care.

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scientific method

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• University of Nevada, Reno - College of Agriculture, Biotechnology and Natural Resources Extension - The Scientific Method
• World History Encyclopedia - Scientific Method
• LiveScience - What Is Science?
• Verywell Mind - Scientific Method Steps in Psychology Research
• WebMD - What is the Scientific Method?
• Chemistry LibreTexts - The Scientific Method
• National Center for Biotechnology Information - PubMed Central - Redefining the scientific method: as the use of sophisticated scientific methods that extend our mind
• Khan Academy - The scientific method
• Simply Psychology - What are the steps in the Scientific Method?
• Stanford Encyclopedia of Philosophy - Scientific Method

scientific method , mathematical and experimental technique employed in the sciences . More specifically, it is the technique used in the construction and testing of a scientific hypothesis .

The process of observing, asking questions, and seeking answers through tests and experiments is not unique to any one field of science. In fact, the scientific method is applied broadly in science, across many different fields. Many empirical sciences, especially the social sciences , use mathematical tools borrowed from probability theory and statistics , together with outgrowths of these, such as decision theory , game theory , utility theory, and operations research . Philosophers of science have addressed general methodological problems, such as the nature of scientific explanation and the justification of induction .

The scientific method is critical to the development of scientific theories , which explain empirical (experiential) laws in a scientifically rational manner. In a typical application of the scientific method, a researcher develops a hypothesis , tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments. The modified hypothesis is then retested, further modified, and tested again, until it becomes consistent with observed phenomena and testing outcomes. In this way, hypotheses serve as tools by which scientists gather data. From that data and the many different scientific investigations undertaken to explore hypotheses, scientists are able to develop broad general explanations, or scientific theories.

See also Mill’s methods ; hypothetico-deductive method .

Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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What is Research?

Research is an often-misused term, its usage in everyday language very different from the strict scientific meaning.

This article is a part of the guide:

• Definition of Research
• Research Basics
• Steps of the Scientific Method
• Purpose of Research
• What is the Scientific Method?

Browse Full Outline

• 1 Research Basics
• 2.1 What is Research?
• 2.2 What is the Scientific Method?
• 2.3 Empirical Research
• 3.1 Definition of Research
• 3.2 Definition of the Scientific Method
• 3.3 Definition of Science
• 4 Steps of the Scientific Method
• 5 Scientific Elements
• 6 Aims of Research
• 7 Purpose of Research
• 8 Science Misconceptions

In the field of science, it is important to move away from the looser meaning and use it only in its proper context. Scientific research adheres to a set of strict protocols and long established structures.

Definition of the Scientific Method

Often, we will talk about conducting internet research or say that we are researching in the library. In everyday language, it is perfectly correct grammatically, but in science , it gives a misleading impression. The correct and most common term used in science is that we are conducting a literature review .

The Guidelines

What is research ? For a successful career in science, you must understand the methodology behind any research and be aware of the correct protocols.

Science has developed these guidelines over many years as the benchmark for measuring the validity of the results obtained.

Failure to follow the guidelines will prevent your findings from being accepted and taken seriously. These protocols can vary slightly between scientific disciplines, but all follow the same basic structure.

Aims of Research

The general aims of research are:

Observe and Describe

Determination of the Causes

Purpose of Research - Why do we conduct research? Why is it necessary?

Steps of the Scientific Process

The steps of the scientific process has a structure similar to an hourglass - The structure starts with general questions, narrowing down to focus on one specific aspect , then designing research where we can observe and analyze this aspect. At last, the hourglass widens and the researcher concludes and generalizes the findings to the real world.

• Summary of the Elements in Scientific Research

1) Setting a Goal

Research in all disciplines and subjects, not just science, must begin with a clearly defined goal . This usually, but not always, takes the form of a hypothesis .

For example, an anthropological study may not have a specific hypothesis or principle, but does have a specific goal, in studying the culture of a certain people and trying to understand and interpret their behavior.

The whole study is designed around this clearly defined goal, and it should address a unique issue, building upon previous research and scientifically accepted fundamentals. Whilst nothing in science can be regarded as truth, basic assumptions are made at all stages of the research, building upon widely accepted knowledge.

2) Interpretation of the Results

Research does require some interpretation and extrapolation of results.

In scientific research, there is always some kind of connection between data (information gathered) and why the scientist think that the data looks as it does. Often the researcher looks at the data gathered, and then comes to a conclusion of why the data looks like it does.

A history paper, for example, which just reorganizes facts and makes no commentary on the results, is not research but a review .

If you think of it this way, somebody writing a school textbook is not performing research and is offering no new insights. They are merely documenting pre-existing data into a new format.

If the same writer interjects their personal opinion and tries to prove or disprove a hypothesis , then they are moving into the area of genuine research. Science tends to use experimentation to study and interpret a specific hypothesis or question, allowing a gradual accumulation of knowledge that slowly becomes a basic assumption.

3) Replication and Gradual Accumulation

For any study, there must be a clear procedure so that the experiment can be replicated and the results verified.

Again, there is a bit of a grey area for observation-based research , as is found in anthropology, behavioral biology and social science, but they still fit most of the other criteria.

Planning and designing the experimental method , is an important part of the project and should revolve around answering specific predictions and questions . This will allow an exact duplication and verification by independent researchers, ensuring that the results are accepted as real.

Most scientific research looks at an area and breaks it down into easily tested pieces.

The gradual experimentation upon these individual pieces will allow the larger questions to be approached and answered, breaking down a large and seemingly insurmountable problem, into manageable chunks.

True research never gives a definitive answer but encourages more research in another direction. Even if a hypothesis is disproved, that will give an answer and generate new ideas, as it is refined and developed.

Research is cyclical, with the results generated leading to new areas or a refinement of the original process.

4) Conclusion

The term, research , is much stricter in science than in everyday life.

It revolves around using the scientific method to generate hypotheses and provide analyzable results. All scientific research has a goal and ultimate aim , repeated and refined experimentation gradually reaching an answer.

These results are a way of gradually uncovering truths and finding out about the processes that drive the universe around us. Only by having a rigid structure to experimentation, can results be verified as acceptable contributions to science.

Some other areas, such as history and economics, also perform true research, but tend to have their own structures in place for generating solid results. They also contribute to human knowledge but with different processes and systems.

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Martyn Shuttleworth (Feb 2, 2008). What is Research?. Retrieved Aug 20, 2024 from Explorable.com: https://explorable.com/what-is-research

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September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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Researchers cannot make wild theories such as a link between taking a vaccine and becoming happier. If they want this to be accepted by the scientific community, scientific research evidence is needed. And still, we can only assume it is the current temporary truth. So, really in psychology , there is no end-game. Thus, scientific research aims to prove or disprove existing theories. 

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• Schizophrenia
• Scientific Foundations of Psychology
• Scientific Investigation
• Sensation and Perception
• Social Context of Behaviour
• Social Psychology
• We will kick off our learning by understanding the concepts of the scientific method of research, including the aims of scientific research.
• Then, we will explore the steps of scientific research generally taken in psychology .
• And finally, we will look at the types of scientific research and some scientific research examples.

Scientific Method of Research

Scientific research follows a systematic approach. It aims to acquire new information that adds to the existing knowledge in the research field. The consensus of scientific research is that researchers should plan their investigation before executing it.

This is important as it can help identify if research is observable, empirical, objective, valid, and reliable. These are the key features of scientific research.

But how can we tell if research is scientific?

Similar to how products are quality assessed before they reach customers, research is assessed using quality criteria. The quality criteria standards of qualitative and quantitative research differ.

For example, validity, reliability, empiricalness and objectivity are essential in quantitative research. On the other hand, transferability, credibility and confirmability are essential in qualitative research.

The two types of research have different quality criteria because of their different natures. Quantitative research focuses on the facts. But, qualitative research focuses on participants' subjective experiences.

A ims of Scientific Research

Scientific research aims to identify and build scientific knowledge that discovers and explains laws or principles of natural or social phenomena. T here tend to be multiple explanations proposed by various researchers to explain a phenomenon. The aim of scientific research is to either provide supporting evidence or disprove them.

The reasons why it is important for research to be scientific are:

• It leads to the progression of our understanding of a phenomenon. Based on these findings, researchers can outline the motivations/drives concerning individuals' thoughts and behaviours. They can also discover how illnesses occur and progress or how to treat them.
• Since research is used, for example, to test the effectiveness of a treatment, it is crucial to ensure that it is based on scientific and empirical data. This ensures that people get the correct treatment to improve their condition.
• Scientific research ensures that the findings collected are reliable and valid. Reliability and validity are essential because they guarantee that the results apply to the target population and that the investigation measures what it intends.

This process is what causes the progression of knowledge in the scientific fields.

Steps of Scientific Research

For research to be scientific, it should follow a specific process. Following this process ensures that the investigation is empirical and observable. It also increases the likelihood of the researcher measuring variables in a reliable, valid, and objective manner.

The seven stages that research should follow to be scientific are:

• Make an observation: observe an interesting phenomenon.
• Ask a question: based on the observation, form a research question.
• Form a hypothesis: after formulating the research question, the researcher should identify and operationalise the tested variables . These variables form a hypothesis: a testable statement concerning how the research will investigate the research question.

Popper argued that hypotheses should be falsifiable, meaning they should be written in a testable way and can be proven wrong. If researchers predict unicorns make children happier, this is not falsifiable as this can't be empirically investigated.

• Make a prediction based on the hypothesis: researchers should conduct background research before conducting research and make a guess/prediction of what they expect to happen when testing the hypothesis.
• Test the hypothesis: carry out empirical research to test the hypothesis.
• Analyse the data: the researcher should analyse the gathered data to identify if it supports or rejects the hypothesis proposed.
• Conclusions: the researcher should state whether the hypothesis was accepted or rejected, provide general feedback on their research (strengths/weaknesses), and acknowledge how the results will be used to make new hypotheses. This will indicate the next direction that research should take to add to the psychology research field.

Once research has been conducted, a scientific report should be written. A scientific research report should include an introduction, procedure, results, discussion and references. These sections must be written according to the American Psychological Association guidelines.

Types of Scientific Research

Psychology is often regarded as a fragmented subject. In biology, a natural science, usually one method, experimentation, is used to prove or disprove a theory, but this is not the case in psychology.

There are various approaches in psychology , each of which has a preference and disregards specific assumptions and research methods .

Biological psychologists have a preference towards experimental methods and disregard principles of the role of nurture.

The approaches in psychology are described as paradigms by Kuhn. He argued that the popular and accepted paradigm is based on which approach is best and most suited to explain the current theories.

When an approach can no longer explain the current phenomenon, there is a paradigm shift, and a more suited approach becomes accepted.

Scientific research can be classified based on different categorising systems. For example, whether the study uses primary or secondary data, what type of causality relationship the data provides, or the research setting. This next section will explain the different types of scientific research used in psychology.

The three main ways of categorising research are to identify the purpose of the research:

• Exploratory research aims to investigate new phenomena that have not been previously investigated or have limited research. It tends to be used as an initial stage to identify potential variables to understand a phenomenon.
• Descriptive research examines questions regarding the whats, whens, and where of phenomena. For example, to describe how variables are related to a phenomenon.
• Analytical research provides explanatory findings of phenomena. It finds and explains causal relationships between variables.

Scientific Research: Causality

Descriptive research allows researchers to identify similarities or differences and describe the data. This type of research can describe the research findings but cannot be used to explain why the results occurred.

Examples of descriptive research include:

• Descriptive statistics include the mean, median, mode, range, and standard deviation.
• A case report is a study that investigates a phenomenon of a unique characteristic observed in an individual.
• Epidemiological research explores the prevalence of epidemiology (diseases in the population).

What's important to note is that causality can be inferred from this type of scientific research.

Researchers use analytical research to explain why phenomena occur. They usually use a comparison group to identify differences between the experimental groups.

Researchers can infer causality from experimental, analytical research. This is because of its scientific nature, as the researcher experiments in a controlled setting. Scientific research involves manipulating an independent variable and measuring its effect on the dependent variable whilst controlling external factors.

As external influences are controlled, researchers can say with confidence (but not 100%) that the observed results are due to the manipulation of the independent variable.

In scientific research, the independent variable is thought of as the phenomenon's cause, and the dependent variable is theorised as the effect.

Scientific Research Examples

Research can be identified as primary or secondary research. This can be determined by whether the data used for analysis is collected themself or if they use previously published findings.

Primary research is data collected and analysed by themselves.

Some examples of primary scientific research are:

• Laboratory experiments - research carried out in a controlled environment.
• Field research - research carried out in a real-life setting. Here the researcher manipulates the independent variable.
• Natural experiments - research conducted in a real-life setting with no intervention from the researcher.

Although these examples are all regarded as scientific research, laboratory experiments are considered the most scientific and natural experiments the least. As in lab experiments, the researchers have the most control, and natural experiments have the least.

Now secondary research is the opposite of primary; it involves using previously published research or data to support or negate a hypothesis.

Some examples of secondary scientific research are:

• A meta-analysis - uses statistical means to combine and analyse data from multiple studies that are similar.
• A systematic review uses a systematic approach (clearly defining variables and creating extensive inclusion and exclusion criteria to find research in databases) to gather empirical data and answer a research question.
• A review is when the researcher critiques another researcher's published work.

Similarly, these are considered scientific; however, many critiques of these research methods concern the researchers limited control and how this can later affect the study's reliability and validity.

Scientific Research - Key takeaways

• The scientific method of research suggests that research should checkmark the following criteria: empirical, objective, reliable and valid.
• The aims of scientific research are to build scientific knowledge that discovers and explains laws or principles of natural or social phenomena.

In general, there are seven steps of scientific research.

Primary scientific research examples include lab, field and natural experiments and secondary scientific research examples include meta-analyses, systematic reviews and reviews.

Laboratory experiments are considered the most 'scientific' type of scientific research.

Flashcards in Scientific Research 155

• Make an observation.
• Ask a question.
• Form a hypothesis.
• Make a prediction based on the hypothesis.
• Test the hypothesis.
• Analyse the data.
• Draw a conclusion.
• Explorative 
• Descriptive 

Primary research is research that the investigator conducts.

Secondary research is research using and analysing previously published studies to understand phenomena.

Research is a data collection and analysis method used to add to our existing knowledge. But the difference is that scientific research follows a systematic approach to acquiring new information that adds to the current knowledge in the research field. This research is required to be observable, objective and empirical. 

Descriptive research cannot explain why a phenomenon occurs. i.e., provide causality explanations.

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Frequently Asked Questions about Scientific Research

What is the scientific research process?

In general, there are seven steps of scientific research. These aim to ensure that scientific research is reliable, valid, objective and empirical. 

What is the difference between research and scientific research?

Research is a data collection and analysis method used to add to our existing knowledge. But the difference is that scientific research follows a systematic approach to acquiring new information that adds to the current knowledge in the research field. This research is required to be observable, objective and empirical. 

What are the examples of scientific research?

Primary scientific research examples include lab, field and natural experiments; secondary scientific research examples include meta-analyses, systematic reviews and reviews. 

What are the seven stages of scientific research?

• Drawing conclusions.

What is scientific research and why is it important?

Scientific research is defined as research that follows a systematic approach to acquiring new information that adds to the existing knowledge in the research field. 

Research must be scientific because it leads to the progression of our understanding of phenomena. 

Test your knowledge with multiple choice flashcards

Scientific research is what causes the progression of knowledge in the scientific fields. True or false? 

How many stages of scientific research are there? 

Which type of scientific research provides explanatory findings of phenomena and explains causal relationships between variables?

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Understanding Scientific Research – A Comprehensive Guide

• By Kevin Jubbal, M.D.
• January 18, 2020
• Accompanying Video , Pre-med
• High School , Reading , Research

Reading primary literature – meaning research articles – is intimidating, confusing, and seems out of reach for most people who aren’t trained scientists. But it doesn’t have to be that way. Let’s cover how to make reading research articles easy, fun, and approachable.

As Richard Feynman once said, “the first principle is that you must not fool yourself — and you are the easiest person to fool.” We’ll equip you with the tools and strategies to not be fooled with regards to scientific research moving forward.

As part of my neuroscience major in college, we were required to read dozens of research articles related to the field. We spent hours going over every single article, dissecting its strengths, weaknesses, and working to accurately assess what value the paper provided to the scientific community.

Yet despite reading dozens of these neuroscience papers, when I entered medical school, I still didn’t enjoy reading the primary literature. In fact, I avoided doing so unless absolutely necessary. It wasn’t until I began doing research of my own, read hundreds of papers, and published dozens of my own include a scrolling screenshot or recording of my own publication list at kevinjubbal.com that it all began to click. Being able to understand and assess the scientific literature is so important to parse out the noise from the truth, but it doesn’t have to take you years as it did for me.

1 | The Types of Research Studies

When it comes to scientific studies, there are different levels of evidence. Not all studies are created equal, and the study design is a big part of how strong the evidence is.

At the top, randomized controlled trials are the gold standard, the cream of the crop. Below that, prospective cohort and case-control studies. Prospective means you follow the subjects over time to see the outcomes of interest. Third, we have retrospective cohort or case-control studies, meaning you already have the outcomes of interest, but look back historically and make interpretations. Fourth, we have case series and case reports, which are investigations into individual patient cases. There are other levels, such as systematic reviews, meta-analyses, expert opinion, and others, but for simplicity, we’ll stick to these four levels.

This ranking may not make sense just yet, and that’s ok. We’ll now cover the elements of research, and how they apply to each type of research study, and it will all begin to come together.

Epidemiology , coming from the Greek term epidēmia, translates to “prevalence of disease”. It is the branch of medicine dealing with the incidence, distribution, and control of diseases. If the primary aim of science is discovering the truth and determining cause and effect, then it’s important to note that most observational epidemiological studies cannot establish causality, and therefore they cannot soundly accept or reject a hypothesis. Strong correlations found in observational studies can be compelling enough to take seriously, but there are limitations.

When it comes to observational studies , compared to experimental studies, we have cohort, case-control, and cross-sectional. Without diving into the differences of each type of observational study, understand this generally entails observing large groups of individuals and recording their exposure to risk factors to find associations with possible causes of disease. If they’re retrospective, they’re looking back in time to identify particular characteristics associated with the outcome of interest. These types of studies are prone to confounding and other biases, which can take us further from the truth. We’ll cover this in more detail shortly. Prospective cohort studies recruit subjects and collect baseline information before the subjects have developed the outcome of interest. The advantage of prospective studies is they reduce several types of biases that are commonplace in retrospective studies.

There are four steps to the scientific method :

• Make an observation
• Come up with a (falsifiable) hypothesis based on this observation
• Test the hypothesis through an experiment
• Accept or reject hypothesis based on experiment results

To determine causality, meaning if a cause results in an effect (like whether or not red meat causes cancer), the hypothesis must be adequately tested. This is the part that is most commonly overlooked , particularly in disciplines such as nutrition, because doing experiments necessary to establish causality presents several obstacles. For that reason, many researchers turn to doing easier observational studies, and I’m guilty of this too, but the problem is that most of these don’t get us closer to the truth.

The gold standard for determining causality is a well designed randomized controlled trial, or RCT for short. The researchers create inclusion and exclusion criteria to gather a group of subjects qualified for the study. Then, they randomize subjects into two groups. For example, one group receives drug A, and the other group receives a placebo.

By randomly allocating participants into the treatment or control group, much of the bias from observational studies is substantially reduced. In short, finding cause and effect becomes much easier. If randomized controlled trials are so much better, then why aren’t they always used?

First, they can be very expensive. One report looking at all RCTs funded by the US National Institute of Neurological Disorders and Stroke found 28 trials with a total cost of $335 million.

Second, RCTs take a long time. According to one study , the median time from the start of enrollment to publication was 5 and a half years .

Third, not all RCTs are created equal, and it’s quite challenging to conduct a high-quality RCT. These studies must have adequate randomization, stratification, blinding, sample size, power, proper selection of endpoints, clearly defined selection criteria, and more.

Fourth, ethical considerations. If you’re assigning someone to be in the control or experimental group, you can assign them to something you think will be helpful, like a medication or other treatment, or not have an effect, like placebo or control group. But you wouldn’t be able to assign someone to a group that you would expect to harm them – can you imagine assigning some teenagers to smoke cigarettes and some not to? This is a key distinction between RCTs and observational studies. While RCTs seek to establish cause-and-effect relationships that are beneficial, epidemiologists seek to establish associations that are harmful.

2 | Relative Risk vs Absolute Risk

To better understand the strengths and weaknesses of any particular research study, we’ll need to explore statistics. Don’t worry, we’ll keep it to basic statistics, nothing too crazy.

Relative risk , in its simplest terms, is the relative difference in risk between two groups. If a certain drug decreases the risk of colon cancer from 0.2% to 0.1%, that’s a 50% relative risk reduction. Decreasing the initial risk, 0.2%, by 50%, gives you a risk of 0.1%. The actual change in the rate of the event occurring would be the absolute risk reduction , which in this instance would be 0.1%, because 0.2% – 0.1% = 0.1%.

The way most studies, and especially journalists, summarize and report the results is through relative risk changes. This is much more headline-worthy but obscures the truth where the absolute risk would be more useful at communicating true impact. But what’s more likely to get clicks? “New drug reduces colon cancer risk by 50%!” That would be relative risk reduction. Alternatively, “New drug reduces colon cancer risk from 2 per 1000 to 1 per 1000”. That would be absolute risk reduction.

3 | Confounding & Biases

In the world of research, bias is anything that causes false conclusions and is potentially misleading.

Let’s start with one of the biggest offenders: confounding .

A confounding variable is one that influences both the independent and dependent variables but wasn’t accounted for in the study. For example, let’s say we’re studying the correlation between bicycling and the sale of ice-cream. As the bicycling rate increases, so does the sale of ice cream. The researchers conclude that bicycling causes people to consume ice cream. The third variable, weather, confounds the relationship between bicycling and ice cream, as when it’s hot outside, people are more likely to bicycle and also more likely to buy ice cream.

Another bias that isn’t properly appreciated, particularly in the world of nutrition, is the healthy user bias . Health-conscious people are more likely to do certain activities. For example, most health-conscious people have heard that red meat is bad, and therefore they’re less likely to eat red meat. People who eat more red meat are less health-conscious, and therefore are also more likely to smoke, not exercise, and consume soft drinks. When an observational study comes out comparing those who eat red meat to those who don’t, we cannot actually conclude it’s due to the red meat and not these other factors. Even when researchers are aware of these factors, they are virtually impossible to properly account for.

Selection bias refers to the study population not being representative of the target population, usually due to errors in the selection of subjects into a study, or the likelihood of them staying in the study. In the “ lost to follow-up” bias , researchers are unable to follow up with certain subjects, so they don’t know what happened to them, such as whether they developed the outcome of interest. This leads to a selection bias when the loss to follow up is not the same across the exposed and unexposed groups.

There are many other biases, but we don’t have time to explore each and everyone here.

4 | Randomization & Statistics

Good research minimizes the effects of confounding and biases. How do we do that?

Randomization is a method where study participants are randomly assigned to a treatment or control group. Randomization is a key part of being able to distinguish cause and effect, as proper randomization eliminates confounding. You cannot do this in observational studies, as subjects self-select themselves into whichever group.

When confounding variables are inevitably present, there are statistical methods to “control” or “adjust for” the confounders. The two are stratification and multivariate models.

Stratification fixes the level of the confounders and produces subgroups within which the confounder does not vary. This allows for evaluation of the exposure-outcome association within each stratum of the confounder. This works because the confounder does not vary across the exposure-outcome at each level.

Multivariate models are better at controlling for a greater number of confounders. There are various types, one of the most common of which is linear regression . In its simplest terms, regression is fitting the best straight line to a dataset. Think back to algebra and y = mx + b. We’re trying to find the equation that best predicts the linear relationship between the observed data, being y, and the experimental variable, being x. Logistical regression deals with more complex relationships with multiple continuous variables.

The important thing to note is that confounding often still persists, even after adjustment. There are almost an infinite number of possibilities that can confound an observation, but researchers can only eliminate or control for the ones they are aware of.

Alex Reinhart, author of Statistics Done Wrong, points out that it’s common to interpret results by saying, “If weight increases by one pound, with all other variables held constant, then heart attack rates increase by X percent. You can quote the numbers from the regression equation, but in the real world, the process of gaining a pound of weight also involves other changes. Nobody ever gains a pound with all other variables held constant, so your regression equation doesn’t translate to reality.”

Because confounding is such a central limitation to observational research, we must be careful when drawing conclusions from these types of studies. With observational epidemiology, it’s incredibly difficult to prove an association right or wrong. While a small minority of these associations may be causal, the overwhelming majority are not. Therefore, we should err on the side of skepticism.

5 | Power & Significance

When you propose a hypothesis in a research study, there are two forms: the null hypothesis , meaning there is no relationship between the two phenomena, and the alternative hypothesis , meaning there is a relationship. The study seeks to provide data to suggest one over the other — note that science doesn’t prove things, as you could in math, but rather provides evidence for or against.

The p -value is the scoring metric that makes the final call. It’s the probability of obtaining test results from chance alone, assuming the null hypothesis is correct. In other words, it’s the likelihood that no relationship exists, but the findings occurred due to chance alone. A smaller p -value more strongly rejects a null hypothesis. A larger p -value means a larger chance that the effect you are seeing is due to chance, thus supporting the null hypothesis.

A p -value cutoff is assigned by the researchers to determine the cutoff at which statistical significance is achieved. We call this number α, and it is usually set to 0.05, meaning 5%, or sometimes lower. If the p-value is less than 0.05, we say the results are “statistically significant,” and the null hypothesis is rejected.

There’s a chance we’re wrong, and we have terms for this, too. When there’s no true effect, but we think there is, we call this a false positive, or a Type I error . We failed to reject the null hypothesis even when it was true. The opposite, where there is an effect but we think there isn’t, is called a Type II error . We accepted the null hypothesis when we shouldn’t have. The chance of committing a Type II error is called β.

Statistical power is the probability that a study will correctly find a real effect, meaning a true positive. This translates to Power = 1 – β. Power is influenced by four factors:

• Probability of a false positive (α, or Type I error rate)
• Sample size (N)
• Effect size (the magnitude of difference between groups)
• Probability of a false negative (β, or Type II error rate)

Keep this in mind, as we’ll be coming back to it.

A corollary to p -values are confidence intervals . To find the confidence interval, you take 1 – α, so if α is commonly set to 0.05, the confidence interval would be 0.95, or 95%. When reading a study, you can quickly determine if statistical significance was achieved by whether or not the confidence intervals include the number 1.00. If it’s larger, like 1.05 – 1.27, then a positive association is present with statistical significance, and if it’s smaller, like 0.56 – 0.89, then a negative association is present with statistical significance.

Confidence intervals are commonly misunderstood. With a 95% confidence interval of 1.05 – 1.27, this doesn’t mean that we are 95% confident that the true effect is between 1.05-1.27. Rather, if we were to take 100 different samples and compute a 95% confidence interval for each sample, then 95 of the 100 confidence intervals will contain the true value. In other words, a 95% confidence interval states that 95% of experiments conducted in this exact manner will include the true value, but 5% will not.

Lastly, let’s clarify statistical significance versus practical significance . A study can find statistical significance but have no practical significance. This is more common than you think. A common case where this happens is when the sample size is too large. The larger the sample size, the greater the probability the study will reach statistical significance. At these extremes, even minute differences in outcomes can be statistically significant. If a study finds that a new intervention reduces weight by 0.5 pounds, who cares? It’s not clinically relevant.

The reverse is also true, where a study demonstrates practical significance, yet was unable to achieve statistical significance. If we revisit the four factors that influence power, we see that sample size is the most easily manipulated to over- or underpower a study. Often times, observational studies are overpowered with thousands of subjects, such that any minute difference may yield a statistically significant result. Other studies experience the opposite, whereby they have a small number of subjects, and even if there is a real difference, statistical significance cannot be demonstrated.

Each of these components in isolation isn’t enough to make you an expert at deciphering research studies. However, when you put each piece in context and understand the why of how sound science is conducted, you’ll become far better equipped to think critically and make sense of the primary literature yourself, without having to rely on lazy thinking and black and white summaries from journalists.

If you made it to the end of this post, congratulations! This was an incredibly challenging post to make, as there’s so much to research, but I hope you learned something that will make reading research articles in the future easier and more productive.

Kevin Jubbal, M.D.

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TODAY'S HOURS:

Research Process

• Select a Topic
• Find Background Info
• Focus Topic
• List Keywords
• Search for Sources
• Evaluate & Integrate Sources
• Cite and Track Sources

What is Scientific Research?

Research study design, natural vs. social science, qualitative vs. quantitative research, more information on qualitative research in the social sciences, acknowledgements.

Thank you to Julie Miller, reference intern, for helping to create this page.

Some people use the term research loosely, for example:

• People will say they are researching different online websites to find the best place to buy a new appliance or locate a lawn care service.
• TV news may talk about conducting research when they conduct a viewer poll on current event topic such as an upcoming election.
• Undergraduate students working on a term paper or project may say they are researching the internet to find information.
• Private sector companies may say they are conducting research to find a solution for a supply chain holdup.

However, none of the above is considered “scientific research” unless:

• The research contributes to a body of science by providing new information through ethical study design or
• The research follows the scientific method, an iterative process of observation and inquiry.

The Scientific Method

• Make an observation: notice a phenomenon in your life or in society or find a gap in the already published literature.
• Ask a question about what you have observed.
• Hypothesize about a potential answer or explanation.
• Make predictions if our hypothesis is correct.
• Design an experiment or study that will test your prediction.
• Test the prediction by conducting an experiment or study; report the outcomes of your study.
• Iterate! Was your prediction correct? Was the outcome unexpected? Did it lead to new observations?

The scientific method is not separate from the Research Process as described in the rest of this guide, in fact the Research Process is directly related to the observation stage of the scientific method. Understanding what other scientists and researchers have already studied will help you focus your area of study and build on their knowledge.

Designing your experiment or study is important for both natural and social scientists. Sage Research Methods (SRM) has an excellent "Project Planner" that guides you through the basic stages of research design. SRM also has excellent explanations of qualitative and quantitative research methods for the social sciences.

For the natural sciences, Springer Nature Experiments and Protocol Exchange have guidance on quantitative research methods.

Books, journals, reference books, videos, podcasts, data-sets, and case studies on social science research methods.

Sage Research Methods includes over 2,000 books, reference books, journal articles, videos, datasets, and case studies on all aspects of social science research methodology. Browse the methods map or the list of methods to identify a social science method to pursue further. Includes a project planning tool and the "Which Stats Test" tool to identify the best statistical method for your project. Includes the notable "little green book" series (Quantitative Applications in the Social Sciences) and the "little blue book" series (Qualitative Research Methods).

Platform connecting researchers with protocols and methods.

Springer Nature Experiments has been designed to help users/researchers find and evaluate relevant protocols and methods across the whole Springer Nature protocols and methods portfolio using one search. This database includes:

• Nature Protocols
• Nature Reviews Methods Primers
• Nature Methods
• Springer Protocols

Open repository for sharing scientific research protocols. These protocols are posted directly on the Protocol Exchange by authors and are made freely available to the scientific community for use and comment.

Includes these topics:

• Biochemistry
• Biological techniques
• Chemical biology
• Chemical engineering
• Cheminformatics
• Climate science
• Computational biology and bioinformatics
• Drug discovery
• Electronics
• Energy sciences
• Environmental sciences
• Materials science
• Molecular biology
• Molecular medicine
• Neuroscience
• Organic chemistry
• Planetary science

Qualitative research is primarily exploratory. It is used to gain an understanding of underlying reasons, opinions, and motivations. Qualitative research is also used to uncover trends in thought and opinions and to dive deeper into a problem by studying an individual or a group.

Qualitative methods usually use unstructured or semi-structured techniques. The sample size is typically smaller than in quantitative research.

Example: interviews and focus groups.

Quantitative research is characterized by the gathering of data with the aim of testing a hypothesis. The data generated are numerical, or, if not numerical, can be transformed into useable statistics.

Quantitative data collection methods are more structured than qualitative data collection methods and sample sizes are usually larger.

Example: survey

Note: The above descriptions of qualitative and quantitative research are mainly for research in the Social Sciences, rather than for Natural Sciences as most natural sciences rely on quantitative methods for their experiments.

Qualitative research is approaching the world in its natural setting and in a way that reveals the particularities rather than doing studies in a controlled setting. It aims to understand, describe, and sometimes explain social phenomena in a number of different ways:

• Experiences of individuals or groups
• Interactions and communications
• Documents (texts, images, film, or sounds, and digital documents)
• Experiences or interactions

Qualitative researchers seek to understand how people conceptualize the world around them, what they are doing, how they are doing it or what is happening to them in terms that are significant and that offer meaningful learnings.

Qualitative researchers develop and refine concepts (or hypotheses, if they are used) in the process of research and of collecting data. Cases (its history and complexity) are an important context for understanding the issue that is studied. A major part of qualitative research is based on text and writing – from field notes and transcripts to descriptions and interpretations and finally to the presentation of the findings and of the research as a whole.

For more information, see:

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• Last Updated: Aug 12, 2024 2:59 PM
• URL: https://libguides.umflint.edu/research

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What is Research?

Research is the pursuit of new knowledge through the process of discovery. Scientific research involves diligent inquiry and systematic observation of phenomena. Most scientific research projects involve experimentation, often requiring testing the effect of changing conditions on the results. The conditions under which specific observations are made must be carefully controlled, and records must be meticulously maintained. This ensures that observations and results can be are reproduced. Scientific research can be basic (fundamental) or applied. What is the difference? The National Science Foundation uses the following definitions in its resource surveys:

Basic research:

The objective of basic research is to gain more comprehensive knowledge or understanding of the subject under study, without specific applications in mind. In industry, basic research is defined as research that advances scientific knowledge but does not have specific immediate commercial objectives, although it may be in fields of present or potential commercial interest.

Applied research:

Applied research is aimed at gaining knowledge or understanding to determine the means by which a specific, recognized need may be met. In industry, applied research includes investigations oriented to discovering new scientific knowledge that has specific commercial objectives with respect to products, processes, or services.

What is research at the undergraduate level?

At the undergraduate level, research is self-directed work under the guidance and supervision of a mentor/advisor ― usually a university professor. A gradual transition towards independence is encouraged as a student gains confidence and is able to work with minor supervision. Students normally participate in an ongoing research project and investigate phenomena of interest to them and their advisor.

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What Is Research and Why We Do It

• First Online: 23 June 2020

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• Carlo Ghezzi 2  

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The notions of science and scientific research are discussed and the motivations for doing research are analyzed. Research can span a broad range of approaches, from purely theoretical to practice-oriented; different approaches often coexist and fertilize each other. Research ignites human progress and societal change. In turn, society drives and supports research. The specific role of research in Informatics is discussed. Informatics is driving the current transition towards the new digital society in which we will live in the future.

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In [ 34 ], P.E. Medawar discusses what he calls the “snobismus” of pure versus applied science. In his words, this is one of the most damaging forms of snobbism, which draws a class distinction between pure and applied science.

Originality, rigor, and significance have been defined and used as the key criteria to evaluate research outputs by the UK Research Excellence Framework (REF) [ 46 ]. A research evaluation exercise has been performed periodically since 1986 on UK higher education institutions and their research outputs have been rated according to their originality, rigor, and significance.

The importance of realizing that “we don’t know” was apparently first stated by Socrates, according to Plato’s account of his thought. This is condensed in the famous paradox “I know that I don’t know.”

This view applies mainly to natural and physical sciences.

Roy Amara was President of the Institute for Future, a USA-based think tank, from 1971 until 1990.

The Turing Award is generally recognized as the Nobel prize of Informatics.

See http://uis.unesco.org/apps/visualisations/research-and-development-spending/ .

Israel is a very good example. Investments in research resulted in a proliferation of new, cutting-edge enterprises. The term start-up nation has been coined by Dan Senor and Saul Singer in their successful book [ 51 ] to characterize this phenomenon.

https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges .

https://ec.europa.eu/programmes/horizon2020/en/h2020-section/cross-cutting-activities-focus-areas .

This figure has been adapted from a presentation by A. Fuggetta, which describes the mission of Cefriel, an Italian institution with a similar role of Fraunhofer, on a smaller scale.

The ERC takes an ecumenical approach and calls the research sector “Computer Science and Informatics.”

I discuss here the effect of “big data” on research, although most sectors of society—industry, finance, health, …—are also deeply affected.

Carayannis, E., Campbell, D.: Mode 3 knowledge production in quadruple helix innovation systems. In: E. Carayannis, D. Campbell (eds.) Mode 3 Knowledge Production in Quadruple Helix Innovation Systems: 21st-Century Democracy, Innovation, and Entrepreneurship for Development. SpringerBriefs in Business, New York, NY (2012)

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Harari, Y.: Homo Deus: A Brief History of Tomorrow. Random House (2016). URL https://books.google.it/books?id=dWYyCwAAQBAJ

Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation (3rd Edition). Addison-Wesley Longman Publishing Co., Inc., USA (2006)

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Medawar, P.: Advice To A Young Scientist. Alfred P. Sloan Foundation series. Basic Books (2008)

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REF2019/2: Panel criteria and working methods (2019). URL https://www.ref.ac.uk/media/1084/ref-2019_02-panel-criteria-and-working-methods.pdf

Senor, D., Singer, S.: Start-Up Nation: The Story of Israel’s Economic Miracle. McClelland & Stewart, Toronto, Canada (2011)

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Ghezzi, C. (2020). What Is Research and Why We Do It. In: Being a Researcher. Springer, Cham. https://doi.org/10.1007/978-3-030-45157-8_1

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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Scientific Method Example

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The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses , and conduct scientific experiments .

A scientific inquiry starts with an observation. Then, the formulation of a question about what has been observed follows. Next, the scientist will proceed through the remaining steps of the scientific method to end at a conclusion.

The six steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. Taking an interest in your scientific discovery is important, for example, if you are doing a science project , because you will want to work on something that holds your attention. Your observation can be of anything from plant movement to animal behavior, as long as it is something you want to know more about.​ This step is when you will come up with an idea if you are working on a science project.

Once you have made your observation, you must formulate a question about what you observed. Your question should summarize what it is you are trying to discover or accomplish in your experiment. When stating your question, be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question could be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you have developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states clearly how you plan to conduct your experiment. It is important you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

Developing a conclusion is the final step of the scientific method. This is where you analyze the results from the experiment and reach a determination about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Scientific Research: What it Means to Me

Affiliation.

• 1 Founder Director, Inter-University Centre for Astronomy and Astrophysics (IUCAA) 1989-2003, Pune; President, Cosmology Commission of the International Astronomical Union, 1994-97. Astrophysicist and Science populariser.
• PMID: 22013355
• PMCID: PMC3190546
• DOI: 10.4103/0973-1229.33003

This article gives a personal perception of the author, of what scientific research means. Citing examples from the lives of all time greats like Newton, Kelvin and Maxwell he stresses the agonies of thinking up new ideas, the urge for creativity and the pleasure one derives from the process when it is completed. He then narrates instances from his own life that proved inspirational towards his research career. In his early studenthood, his parents and maternal uncle had widened his intellectual horizons while in later life his interaction with Fred Hoyle made him take up research challenges away from the beaten path. He concludes that taking up an anti-Establishment stand in research can create many logistical difficulties, but the rewards of success are all the more pleasing.

Keywords: Brian Josephson; Cambridge; Creativity; Eureka; Growing; Hoyle; Intuition; Isaac Newton; Kelvin; Maxwell; Non-Conformism; Seeding; Serendipity.

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Understanding Science

How science REALLY works...

• Understanding Science 101

Scientists strive to test their ideas with evidence from the natural world.

Science relies on evidence

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To learn more about testing by special interest groups and how that can inadvertently lead to bias (and bad science), take an advanced side trip to  Who pays for science?

A SCIENCE PROTOTYPE: RUTHERFORD AND THE ATOM

Ernest Rutherford’s lab tested the idea that an atom’s positive mass is spread out diffusely by firing an alpha particle beam through a piece of gold foil. The evidence resulting from that experiment was a complete surprise: most of the alpha particles passed through the gold foil without changing direction much as expected, but some of the alpha particles came bouncing back in the opposite direction, as though they had struck something dense and solid in the gold foil. If the gold atoms were really like loosely packed snowballs, all of the alpha particles should have passed through the foil – but they did not!

From this evidence, Rutherford concluded that their snowball model of the atom was incorrect, even though it was popular with many other scientists. Instead, the evidence suggested that an atom is mostly empty space and that its positive charge is concentrated in a dense mass at its core, forming a nucleus. When the positively charged alpha particles were fired at the gold foil, most of them passed through the empty space of the gold atoms with little deflection, but a few of them ran smack into the dense, positively charged nucleus of a gold atom and were repelled straight back (like what would happen if you tried to make the north poles of two strong magnets touch). The idea that atoms have positively charged nuclei was also testable. Many independent experiments were performed by other researchers to see if the idea fit with other experimental results.

Rutherford’s story continues as we examine each item on the Science Checklist. To find out how this investigation measures up against the rest of the checklist, read on.

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Just  how  does science test ideas with evidence? For more on how scientific ideas can be supported or refuted by evidence, visit our section on  The core of science: Relating evidence and ideas .

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WATCH LIVE: DNC ceremonial roll call vote to nominate Kamala Harris 

Biden’s right that we need new innovations in cancer care. Here’s what we need more.

At New Orleans’ Tulane University last week, President Joe Biden announced the awarding of $150 million for cancer research through Advanced Research Projects Agency for Health (ARPA-H), another accomplishment of his “Cancer Moonshot.” The award will go to eight teams around the country for projects that aim to improve the methods by which surgeons remove cancerous tumors . In his remarks , Biden stated that the fund has “set its sights on a big idea: calling on researchers and innovators to pioneer new techniques and technologies to make cancer removal more precise, accurate, successful.”

Imagine cancer surgery that removes all the tumor, the first time, without harming healthy cells.

president joe biden speaking at tulane university

“Imagine cancer surgery that removes all the tumor, the first time, without harming healthy cells,” he said.

This announcement marks a goalpost in the Cancer Moonshot work Biden led as vice president, first announced in 2016, the year after his 46-year-old son Beau Biden died of brain cancer .  The original Moonshot promised to “make a decade’s worth of advances in cancer prevention, diagnosis, and treatment, in five years” and “ end cancer as we know it .” President Biden re-emphasized this commitment in 2022, including the more specific goal of reducing the cancer death rate by at least 50% over the next 25 years.

 The term “moonshot” used to refer to something that was highly unlikely . However, after we actually landed on the moon, it came to mean something difficult but achievable. Indeed, cancer experts have said the ambitious goals of Biden’s Cancer Moonshot can be achieved .

Cancer has been the second most common cause of death in the U.S. for nearly 90 years. It’s the leading cause of death for those under the age of 85 . Last year, more than 613,000 people died of cancer, similar to the population of Memphis, Tennessee. More than half of U.S. families have been affected by cancer; 40% of us will be diagnosed with cancer at some point in our lifetimes. President Biden is among those Americans. Not only did he lose his son Beau to brain cancer, but the president and first lady Jill Biden have had cancerous skin lesions identified and successfully removed .

There aren’t that many Americans who think we’ve spent too much money trying to solve cancer; a third or more think we spend too little . However, Biden’s latest announcement regarding increased funding for cancer treatment innovations comes at a time when overall federal funding for scientific agencies is down .

That’s why an announcement of this kind is a golden retriever of health causes: it’s widely endearing, politically popular and it has traditionally been bipartisan. Even so, this Moonshot faces significant challenges.

First, cancer is a bit of a whack-a-mole; its occurrence changes as our environment and population change. The role of specific cancer risks, including factors like obesity and environmental exposures to poor air and toxic waste, shift over time and across geographic locations. Climate-related events influence people’s exposure to cancer risks and access to cancer care . Our population is aging, and, thus, is more vulnerable to cancer. At the same time, increases in cancer among middle-aged and younger people reflect the impact of evolving risk factors. 

Second, though the projects Biden announced funding for last week will employ cool cutting-edge techniques for treatment of cancers, it’s important to remember that solving cancer involves both the front end (prevention and early detection) and the tail end (treatment). “We cannot treat our way out of the rising cancer caseload. The only solution is a full-scale defense, so that nobody suffers the disease in the first place,” Madeline Drexler , editor of Harvard Public Health has written.

A striking emphasis within the data is how much progress is impeded by persistent racial and ethnic health disparities.

This year’s data from the American Cancer Society details declines in cancer mortality achieved largely from reductions in lung cancer, the most common cause of cancer death, driven by reductions in smoking. Treatment innovations, including immunotherapy, have led to improved survival rates for certain types of cancer. However, several of the most common cancers, including those in the breast, pancreas, liver, kidney and prostate, are increasing in incidence, and need a huge boost in prevention efforts. 

Further, a striking emphasis within the ACS data is how much progress is impeded by persistent racial and ethnic health disparities in prevention, access to care and survival rates. For example, rates of uterine corpus cancer, the fourth most common type of cancer among women, have been increasing by about 1% per year overall, but is increasing by more than 2% per year among Black, Hispanic, and Asian American and Pacific Islander women. Alarmingly, survival rates are going down, and within that finding are breathtaking racial disparities: The five-year survival rate for Black women is 63%, but 84% for white women; Black women have more provider visits preceding diagnosis and are less likely to receive appropriate testing; once diagnosed, they are less likely to receive appropriate treatment . As a result, Black women have lower survival rates at every stage of diagnosis.

Although this stage of the Moonshot is about funding scientific discoveries, the fight against cancer cannot be just about discoveries. Dazzling scientific successes don’t mean much if we can’t consistently apply them and if we can’t address the systematic and structural barriers that keep a significant proportion of the population from accessing those discoveries.

“If all innovation ended today and we could just get people access to the innovations that we know about right now, we think we could reduce cancer mortality by another 20 to 30% ,” Karen Knudsen, CEO of the American Cancer Society and the American Cancer Society Cancer Action Network, has said.

The fight against cancer cannot be just about discoveries.

To be fair, the full agenda of the Cancer Moonshot addresses issues of access, prevention, screening, access to care and health inequities , as well as scientific advances. The treatment innovations themselves should demonstrate that they are consciously designed for a heterogenous population, that studies are performed across a broadly representative population and that outcomes are strong across populations defined by race, ethnicity, rurality, income and location. Further, innovation funding should demonstrate an awareness of how the disparities we see in care are reflected in research commitments, too. One study demonstrated that prostate cancer received an average of $1,821,000 per person-years of life lost. Uterine cancer? Just $57,000. 

“Imagine clinical trials that bring innovation to all communities,” said Biden, in his remarks at Tulane. The Cancer Moonshot is like building a rocket; we just need to make sure it can reach every American who needs it.

Esther Choo, M.D. M.P.H., is an emergency medicine physician, health policy researcher and founding member of Equity Quotient, a company that advises organizations on building cultures of equity. She has provided commentary on the pandemic and other health care topics through appearances on MSNBC, CNN, the BBC and Yahoo! Finance and editorials published in The Lancet, the British Medical Journal, The Washington Post, NBC Think and USA Today.

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Stonehenge’s ‘altar stone’ originally came from Scotland and not Wales, new research shows

The mysterious Altar Stone at Stonehenge might have been transported more than 700km from north east Scotland. Scientists have revealed the fascinating study about the stone circle but the revelation brings as many questions as it does answers.

FILE - The world heritage site of Stonehenge is seen in Wiltshire, England on Dec. 17, 2013. (AP Photo/Alastair Grant, File)

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In this photo provided by researchers in August 2024, Stonehenge’s Altar Stone lies underneath two Sarsen stones in Wiltshire, England. (Nick Pearce/Aberystwyth University via AP)

WASHINGTON (AP) — The ancient ritual meaning of Stonehenge is still a mystery, but researchers are one step closer to understanding how the famous stone circle was created.

The unique stone lying flat at the center of the monument was brought to the site in southern England from near the tip of northeast Scotland, researchers reported Wednesday in the journal Nature. It’s not clear whether the 16-foot (5-meter) stone was carried by boat or across land — a journey of more than 460 miles (740 kilometers).

“It’s a surprise that it’s come from so far away,” said University of Exeter archaeologist Susan Greaney, who was not involved in the study.

For more than a hundred years, scientists believed that Stonehenge’s central sandstone slab — long called the “altar stone” — came from much closer Wales. But a study last year by some of the same researchers showed that the stone didn’t match the geology of Wales’ sandstone formations. The actual source of the stone remained unknown until now.

AP AUDIO: Stonehenge’s ‘altar stone’ originally came from Scotland and not Wales, new research shows

AP correspondent Karen Chammas reports on a new study into the origins of the Stone Henge altar stone.

For the study, the team was not permitted to chip away rocks at the site, but instead analyzed minerals in bits of rock that had been collected in previous digs, some dating back to the 1840s. They found a match in the sandstone formations of Orcadian Basin in northeast Scotland, a region that includes parts of the tip of the Scottish peninsula as well as the Orkney Islands.

“That geological ‘fingerprint’ isn’t repeated in any other area of sediment in the U.K.,” said Aberystwyth University geologist Nick Pearce, a study co-author.

Greaney said the difficult logistics of moving the stone such a long distance show a high level of coordination and cultural connection between these two regions of ancient Britain.

Stonehenge was constructed around 5,000 years ago, with stones forming different circles brought to the site at different times. The placement of stones allows for the sun to rise through a stone “window” during summer solstice. The ancient purpose of the altar stone — which lies flat at the heart of Stonehenge, now beneath other rocks — remains a mystery.

“Stonehenge isn’t a settlement site, but a place of ceremony or ritual,” said Heather Sebire, senior curator at English Heritage, who was not involved in the study. She said that past archaeological excavations had not uncovered evidence of feasting or daily living at the site.

Previous research has shown cultural connections — such as similarities in pottery styles — between the area around Stonehenge and Scotland’s Orkney Islands. Other stones at Stonehenge came from western Wales.

While Britain is dotted with other Neolithic stone circles, “the thing that’s unique about Stonehenge is the distance from which the stones have been sourced,” said Aberystwyth University’s Richard Bevins, a study co-author.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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Reforestation to capture carbon could be done much more cheaply, study says

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• New research shows that a mix of natural forest regrowth and tree planting could remove up to 10 times more carbon at $20 per metric ton than previously estimated by the IPCC, the U.N.’s climate science panel.
• The study found that natural regeneration is more cost-effective in 46% of suitable areas, while tree planting is better in 54%, suggesting a tailored approach could maximize carbon capture.
• Researchers estimate that using the most cost-effective method in each location could remove 31.4 billion metric tons of CO2 over 30 years at less than $50 per metric ton.
• While the findings are promising, experts caution that reforestation alone can’t solve the climate crisis and emphasize the need to consider biodiversity and other ecological factors alongside cost-effectiveness.

Trees are allies in the struggle against climate change, and regrowing forests to capture carbon may be cheaper than we thought. According to new research published in Nature Climate Change , a strategic mix of natural regrowth and tree planting could be the most cost-effective way to capture carbon.

Researchers analyzed reforestation projects in 138 low- and middle-income countries to compare the costs of different reforestation approaches. They found it’s possible to remove 10 times more carbon at $20 per metric ton, and almost three times more at $50, compared to what the Intergovernmental Panel on Climate Change (IPCC) had previously estimated .

Neither natural regeneration nor tree planting consistently outperforms the other. Instead, the most cost-effective method varies depending on local conditions. Natural regeneration, which involves letting forests regrow on their own, is cheaper in about 46% of suitable areas. Tree planting, on the other hand, is more cost-effective in 54% of areas.

“Natural regeneration is more cost-effective in areas where tree planting is expensive, regrowing forests accumulate carbon more quickly, or timber infrastructure is distant,” said lead author Jonah Busch, who conducted the study while working for Conservation International. “On the other hand, plantations outperform in areas far from natural seed sources, or where more of the carbon from harvested wood is stored in long-lasting products.”

The research team estimates that by using the cheapest method in each location, we could remove a staggering 31.4 billion metric tons of carbon dioxide from the atmosphere over 30 years, at a cost of less than $50 per metric ton. This is about 40% more carbon removal than if only one method was used universally.

“It’s exciting that the opportunity for low-cost reforestation appears much more plentiful than previously thought; this suggests reforestation projects are worth a second look by communities that might have prejudged them to be cost prohibitive,” said Busch. “While reforestation can’t be the only solution to climate change, our findings suggest it should be a bigger piece of the puzzle than previously thought.”

To reach these conclusions, the research team gathered data from hundreds of reforestation projects and used machine-learning techniques to map costs across different areas at a 1-kilometer (0.6-mile) resolution. This detailed approach allowed them to consider crucial factors such as tree growth rates and potential species in different regions.

Ecologist Robin Chazdon, who wasn’t involved in the research, praised the comprehensive approach but highlighted important considerations beyond cost-effectiveness.

“These eye-opening findings add nuance and complexity to our understanding of the net costs of carbon storage for naturally regenerating forests and monoculture plantations,” Chazdon said. However, she emphasized that “the relative costs of carbon storage should not be the only factor to consider regarding spatial planning of reforestation.”

Chazdon pointed out some of the ecological trade-offs involved in different reforestation methods. Monoculture tree plantations, while potentially cost-effective in certain areas, often create excessive water demand and provide poor opportunities for native biodiversity conservation. In contrast, naturally regenerating forests typically offer a wider range of ecosystem services and better support local biodiversity.

“Ultimately, these environmental costs and benefits — which can be difficult to monetize — need to be incorporated in decisions regarding how and where to grow plantations or foster natural regeneration,” Chazdon said.

The study’s authors acknowledge these limitations and suggest several directions for future research. They propose extending the analysis to high-income countries and exploring other forms of reforestation, such as agroforestry or planting patches of trees and allowing the rest of an area to regrow naturally.

Additionally, the researchers emphasize the need to integrate their findings on cost-effectiveness with data on biodiversity, livelihoods and other societal needs to guide reforestation efforts in different contexts.

While the study’s findings are promising, the researchers caution that reforestation alone won’t solve the climate crisis. Even at its maximum potential, reforestation would only remove as much carbon dioxide in 30 years as eight months of current global emissions.

Reforestation is very important, but it won’t solve climate change on its own, Busch said. Ultimately, “we still need to reduce emissions from fossil fuels.”

Banner image of two men planting trees in the Yokadouma Council Forest, Cameroon. Image courtesy WWF.

Liz Kimbrough  is a staff writer for Mongabay and holds a Ph.D. in ecology and evolutionary biology from Tulane University, where she studied the microbiomes of trees. View more of her reporting  here .

How to pick a tree-planting project? Mongabay launches transparency tool to help supporters decide

Busch, J., Bukoski, J. J., Cook-Patton, S. C., Griscom, B., Kaczan, D., Potts, M. D., … Vincent, J. R. (2024). Cost-effectiveness of natural forest regeneration and plantations for climate mitigation.  Nature Climate Change , 1-7. doi: 10.1038/s41558-024-02068-1

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