Process of Science

Process of Science

What is it about a scientific theory that sets it apart from other ideas? How does one conduct and control a scientific experiment? Why do statistical statements weigh so heavily in science? Scientists often talk about the findings of their research, but they are less likely to talk about the processes and methods that lead to those discoveries. Yet these processes and methods, and the surprises scientists face while pursuing them define science. Our process of science modules use in-depth examples to introduce the methods used by scientists, the ways that scientists work with data and communicate their findings, and the culture and diversity of science.


There is a single scientific method that all scientists follow.

"What is science and how does it work?" This module introduces the Process of Science series, which answers this question, and presents the scientific process as a way of thinking that can help in everyday decision making. A brief overview is given of key concepts that guide the Process of Science module series.

This module explores the nature of scientific knowledge by asking what science is. It emphasizes the importance of a scientific way of thinking and shows how observation and testing add to the body of scientific knowledge. Focusing on astronomy and physics, the module highlights the work of scientists through history who have contributed to our understanding of the age of the universe as a means of conveying the nature of scientific knowledge.

  • Science consists of a body of knowledge and the process by which that knowledge is developed.
  • The core of the process of science is generating testable explanations, and the methods and approaches to generating knowledge are shared publicly so that they can be evaluated by the community of scientists.
  • Scientists build on the work of others to create scientific knowledge.
  • Scientific knowledge is subject to revision and refinement as new data, or new ways to interpret existing data, are found.

Science benefits from diverse interests and different points of view. This module explores at the human side of science. With a look at the unique background and motivations of individual scientists, it is clear how personal experience, varied perspectives, creativity, and even chance contribute to progress in science.

  • Science is a human endeavor. It benefits from the creativity, curiosity, and diligence of individual scientists, and is also subject to human error.
  • Scientists are diverse in many ways, and their personal experiences – including cultures, backgrounds, and chance – influence the paths they follow.
  • Scientists benefit from mentoring and collaboration, working in communities within, or across, institutions and disciplines.

Ethical standards are a critical part of scientific research. Through examples of scientific fraud, misconduct, and mistakes, this module makes clear how ethical standards help ensure the reliability of research results and the safety of research subjects. The importance and consequences of integrity in the process of science are examined in detail.

  • Ethical conduct in science assures the reliability of research results and the safety of research subjects.
  • Ethics in science include: a) standards of methods and process that address research design, procedures, data analysis, interpretation, and reporting; and b) standards of topics and findings that address the use of human and animal subjects in research.
  • Replication, collaboration, and peer review all help to minimize ethical breaches, and identify them when they do occur.

Scientific institutions and societies play an essential role in the process of science and contribute to the building of scientific knowledge. This module explores these different bodies and discusses three types in detail: research institutions, professional societies, and funding institutions. Specific examples highlight how these institutions are essential to progress in science.

  • The community of science includes institutions and professional societies that support scientists physically, financially, and intellectually.
  • Research institutions include universities, national laboratories, government agencies, and corporations that all provide physical space and support for scientific research.
  • Professional societies promote interactions between individuals across institutions by organizing meetings and publications.
  • Governments, private industry, and other institutions provide financial support for scientific research through grants and research contracts.
  • All of these institutions affect the direction of scientific research, and may even bias it, by setting research priorities.

Theories are not based on one scientist's work but on an accumulation of evidence and ideas from many scientists over time. This module discusses how scientific theories are built and revised. It uses the development of the theory of evolution through natural selection to illustrate how theories are built through a process of testing, expanding, and refining.

  • A scientific theory is an explanation inferred from multiple lines of evidence for some broad aspect of the natural world and is logical, testable, and predictive.
  • As new evidence comes to light, or new interpretations of existing data are proposed, theories may be revised and even change; however, they are not tenuous or speculative.
  • A scientific hypothesis is an inferred explanation of an observation or research finding; while more exploratory in nature than a theory, it is based on existing scientific knowledge.
  • A scientific law is an expression of a mathematical or descriptive relationship observed in nature.

Controversy isn't always a bad thing. It exists in every field of science and in many cases clarifies and advances our scientific understanding. This module explains what scientific controversies are and how they differ from other kinds of controversy. Using the example of climate change, the module identifies factors that lead to controversies in science and explains how they are resolved.

  • A scientific controversy is a sustained, public debate among the broader scientific community in which arguments are based on evidence.
  • Controversies cause progress in science by encouraging research on the topic in question.
  • Controversies are resolved when the evidence overwhelmingly favors one argument.
  • Scientific controversies are distinct from political, ethical, and personal controversies, though sometimes they overlap or can have complex interactions.

Success in science requires a creative mind. This module explores the nature of creativity in the scientific process. It details various discoveries and explains how creativity played a significant role in each. The importance of logical thinking and background knowledge to the creative process is discussed.

  • Some of the most important questions in science are either too large or too complex to answer directly, so scientists break them down into smaller, solvable questions.
  • Many times, the questions that scientists research involve the simplest cases.
  • Scientists use creativity to determine which smaller questions are likely to yield results, imagine possible answers to their questions, and devise ways to test those answers.
  • To be creative, scientists need background knowledge, which they gain by learning about past scientific work, talking to colleagues, and tapping their own experience.

Scientists use multiple methods to investigate the natural world and these interconnect and overlap, often with unexpected results. This module gives an overview of scientific research methods, data processing, and the practice of science. It discusses myths that many people believe about the scientific method and provides an introduction to our Research Methods series.

  • The practice of science involves many possible pathways. The classic description of the scientific method as a linear or circular process does not adequately capture the dynamic yet rigorous nature of the practice.
  • Scientists use multiple research methods to gather data and develop hypotheses. These methods include experimentation, description, comparison, and modeling.
  • Scientific research methods are complementary; when multiple lines of evidence independently support one another, hypotheses are strengthened and confidence in scientific conclusions improves.

Manipulating and controlling variables are key aspects that set experimentation apart from other scientific research methods. This module highlights the principles of experimentation through examples from history, including the work of Alhazen in 1000 CE and Louis Pasteur in the 1860s.

  • Experimentation is a research method in which one or more variables are consciously manipulated and the outcome or effect of that manipulation on other variables is observed.
  • Experimental designs often make use of controls that provide a measure of variability within a system and a check for sources of error.
  • Experimental methods are commonly applied to determine causal relationships or to quantify the magnitude of response of a variable.

Observation is an important tool for scientific researchers, and describing what is observed is a valuable method of research. This module explains key features of scientific description and discusses how this method is used in the process of science. Examples from history illustrate the use of description, from the geologic exploration of the US in the 1800s to 20th century studies of primate behavior.

  • Description involves the systematic observation and cataloging of components of a natural system in a manner that can be utilized and replicated by other scientists.
  • Description is commonly used as a research method to explain unique natural systems (such as in ecology or chemistry), large-scale phenomena (such as in astronomy), or past events (such as in geology or forensic science).

Comparing and contrasting is a critical research tool for making sense of the world. Through scenarios in which scientists would likely choose to do comparative research, this module explores the differences and similarities between comparison and experimentation. Studies of the link between cigarette smoking and health illustrate how comparison along with other research methods provided solid evidence that cigarette smoke is a major cause of lung cancer.

  • Comparison is used to determine and quantify relationships between two or more variables by observing different groups that either by choice or circumstance are exposed to different treatments.
  • Comparison includes both retrospective studies that look at events that have already occurred, and prospective studies, that examine variables from the present forward.
  • Comparative research is similar to experimentation in that it involves comparing a treatment group to a control, but it differs in that the treatment is observed rather than being consciously imposed due to ethical concerns, or because it is not possible, such as in a retrospective study.

Scientific modeling is a research method scientists use to replicate real-world systems-whether it's a conceptual model of an atom, a physical model of a river delta, or a computer model of global climate. This module describes the principles that scientists use when building models and shows how modeling contributes to the process of science.

  • Modeling involves developing physical, conceptual, or computer-based representations of systems.
  • Scientists build models to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses.
  • Computer modeling is a relatively new scientific research method, but it is based on the same principles as physical and conceptual modeling.

Data analysis is at the heart of any scientific investigation. Using weather as an example, this module takes readers through the steps of data collection, analysis, interpretation, and evaluation. The module explores how scientists collect and record data, find patterns in data, explain those patterns, and share their research with the larger scientific community.

  • Data collection is the systematic recording of information; data analysis involves working to uncover patterns and trends in datasets; data interpretation involves explaining those patterns and trends.
  • Scientists interpret data based on their background knowledge and experience; thus, different scientists can interpret the same data in different ways.
  • By publishing their data and the techniques they used to analyze and interpret those data, scientists give the community the opportunity to both review the data and use them in future research.

There is uncertainty in all scientific data, and even the best scientists find some degree of error in their measurements. This module uses familiar topics - playing baseball, shooting targets, and calculating the age of an object - to show how scientists identify and measure error and uncertainty, which are reported in terms of confidence.

  • Uncertainty is the quantitative estimation of error present in data; all measurements contain some uncertainty generated through systematic error and/or random error.
  • Acknowledging the uncertainty of data is an important component of reporting the results of scientific investigation.
  • Uncertainty is commonly misunderstood to mean that scientists are not certain of their results, but the term specifies the degree to which scientists are confident in their data.
  • Careful methodology can reduce uncertainty by correcting for systematic error and minimizing random error. However, uncertainty can never be reduced to zero.

Scientific research rarely leads to absolute certainty. There is some degree of uncertainty in all conclusions, and statistics allow us to discuss that uncertainty. Statistical methods are used in all areas of science. The module explores the difference between (a) proving that something is true and (b) measuring the probability of getting a certain result. It explains how common words like "significant," "control," and "random" have a different meaning in the field of statistics than in everyday life.

  • Statistics are used to describe the variability inherent in data in a quantitative fashion, and to quantify relationships between variables.
  • Statistical analysis is used in designing scientific studies to increase consistency, measure uncertainty, and produce robust datasets.
  • There are a number of misconceptions that surround statistics, including confusion between statistical terms and the common language use of similar terms, and the role that statistics employ in data analysis.

Understanding graphs and other visual forms of data is an important skill for scientists. This module describes how to read and interpret graphs and introduces other types of visual data. Various examples demonstrate how trends can be grasped easily when data are shown in a visual form.

  • Visual representations of data are essential for both data analysis and interpretation.
  • Visualization highlights trends and patterns in numeric datasets that might not otherwise be apparent.
  • Understanding and interpreting graphs and other visual forms of data is a critical skill for scientists and students of science.

Using a brief history of scientific writing, this module provides an introduction to the structure and content of scientific journal articles. Key differences between scientific journals and popular media are explained, and basic parts of a scientific article are described through a specific example. The module offers advice on how to approach the reading of a scientific article.

  • Scientists make their research available to the community by publishing it in scientific journals.
  • In scientific papers, scientists explain the research that they are building on, their research methods, data and data analysis techniques, and their interpretation of the data.
  • Understanding how to read scientific papers is a critical skill for scientists and students of science.

Scientific literature is central to the development of science as a whole. This module explains what scientists mean when they refer to the scientific literature and offers specific examples of how scientists use it to (1) discover what other work has been done on a topic, (2) cite sources of their data, and (3) show how their interpretations relate to existing knowledge.

  • The scientific literature provides an archive of research, which scientists make use of throughout the process of investigation.
  • Scientists reference the literature to indicate what other work has been done on a research topic, to cite sources of data that they use, and to show how their interpretations integrate with the published knowledge base of science.
  • New research questions can be investigated by reanalyzing or compiling data from the literature.
  • While individual scientists can make errors, the knowledge base of science as reflected in the scientific literature is self-correcting as new studies and new interpretations come to light.

Peer review is an important part of the process of science. This module describes the history of peer review and shows how the review process helps validate the work of scientists and ensure that quality standards are met. The process is illustrated by actual correspondence among authors, reviewing scientists, and the editor of a scientific journal.

  • Scientific manuscripts and funding proposals are reviewed by several peer scientists who are familiar with the field of research and who make recommendations on whether or not the work should be published and/or funded.
  • Peer review works on many levels and is a fundamental component of the process of science.
  • After publication, scientific papers and other forms of research dissemination are further scrutinized by the scientific community when scientists read or try to reproduce the research.
  • Scientists conduct peer review as part of their responsibility to the scientific community, and are themselves evaluated by the peer review process.

Scientific meetings and conferences play an important role in the process of science. This module describes the history of scientific societies, beginning with the Royal Society of London in 1660. Specific examples illustrate why scientists go to meetings, how these gatherings influence research, and why attending meetings can be important for students and new scientists.

  • Scientific meetings bring scientists from all over the world together to communicate the results of new research.
  • The growth of scientific meetings is closely tied to the growth and development of scientific societies since the 1800s.
  • Individual scientists attend meetings in order to get feedback and disseminate their work, make connections with scientists in their field and beyond, and learn about new research, tools, and ideas.

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