How Does the Field of Science Gain Knowledge and Understanding

The specific questions posed nigh reproducibility and replicability in the committee’s argument of task are part of the broader question of how scientific knowledge is gained, questioned, and modified. In this affiliate, we introduce concepts fundamental to scientific enquiry by discussing the nature of science and outlining cadre values of the scientific procedure. Nosotros outline how scientists accumulate scientific knowledge through discovery, confirmation, and correction and highlight the process of statistical inference, which has been a focus of recently publicized failures to confirm original results.

WHAT IS SCIENCE?

Scientific discipline is a mode of inquiry that aims to pose questions about the world, arriving at the answers and assessing their degree of certainty through a communal effort designed to ensure that they are well grounded.
1

“World,” here, is to exist broadly construed: it encompasses natural phenomena at different fourth dimension and length scales, social and behavioral phenomena, mathematics, and computer scientific discipline. Scientific research focuses on four major goals: (ane) to
describe
the earth (e.g., taxonomy classifications), (ii) to
explicate
the world (e.g., the evolution of species), (3) to
predict
what will happen in the world (e.g., weather forecasting), and (4) to
arbitrate
in specific processes or systems (e.1000., making solar power economical or applied science better medicines).

Human interest in describing, explaining, predicting, and intervening in the world is as sometime every bit humanity itself. People beyond the globe have sought to understand the world and utilize this understanding to accelerate their interests. Long ago, Pacific Islanders used knowledge of the stars to navigate the seas; the Chinese adult earthquake alert systems; many civilizations domesticated and modified plants for farming; and mathematicians effectually the world developed laws, equations, and symbols for quantifying and measuring. With the work of such eminent figures as Copernicus, Kepler, Galileo, Newton, and Descartes, the scientific revolution in Europe in the 16th and 17th centuries intensified the growth in knowledge and agreement of the world and led to ever more than effective methods for producing that very knowledge and understanding.

Over the class of the scientific revolution, scientists demonstrated the value of systematic observation and experimentation, which was a major change from the Aristotelian emphasis on deductive reasoning from ostensibly known facts. Cartoon on this work, Francis Salary (1889 [1620]) adult an explicit structure for scientific investigation that emphasized empirical observation, systematic experimentation, and anterior reasoning to question previous results. Before long thereafter, the concept of communicating a scientific experiment and its result through a written article was introduced past the Majestic Society of London.
2

These contributions created the foundations for the modern practice of science—the investigation of a phenomenon through ascertainment, measurement, and analysis and the critical review of others through publication.

The American Clan for the Advancement for Scientific discipline (AAAS) describes approaches to scientific methods by recognizing the common features of scientific enquiry across the multifariousness of scientific disciplines and the systems each discipline studies (Rutherford and Ahlgren, 1991, p. 2):

Scientific research is not easily described apart from the context of particular investigations. There simply is no stock-still set of steps that scientists always follow, no ane path that leads them unerringly to scientific noesis. There are, however, certain features of scientific discipline that give information technology a distinctive character every bit a mode of enquiry.

Scientists, regardless of their field of study, follow common principles to carry their piece of work: the use of ideas, theories, and hypotheses; reliance on evidence; the use of logic and reasoning; and the advice of results, often through a scientific article. Scientists innovate ideas, develop theories, or generate hypotheses that suggest connections or patterns in nature that can be tested against observations or measurements (i.e., evidence). The drove and characterization of evidence—including the assessment of variability (or doubt)—is cardinal to all of scientific discipline. Analysis of the collected data that leads to results and conclusions about the forcefulness of a hypothesis or proposed theory requires the utilise of logic and reasoning, anterior, deductive, or abductive. A published scientific commodity allows other researchers to review and question the evidence, the methods of collection and analysis, and the scientific results.

While these principles are common to all scientific and engineering science research disciplines, different scientific disciplines use specific tools and approaches that accept been designed to accommodate the phenomena and systems that are particular to each discipline. For case, the mathematics taught to graduate students in astronomy volition be unlike from the mathematics taught to graduate students studying zoology. Laboratory equipment and experimental methods for studying biological science will likely differ from those for studying materials science (Rutherford and Ahlgren, 1991). In general, one may say that different scientific disciplines are distinguished by the nature of the phenomena of interest to the field, the kinds of questions asked, and the types of tools, methods, and techniques used to answer those questions. In improver, scientific disciplines are dynamic, regularly engendering subfields and occasionally combining and reforming. In recent years, for example, what began every bit an interdisciplinary interest of biologists and physicists emerged as a new field of biophysics, while psychologists and economists working together defined a field of behavioral economics. There have been like interweavings of questions and methods for endless examples over the history of science.

No matter how far removed one’south daily life is from the practice of science, the concrete results of science and technology are inescapable. They are manifested in the food people eat, their dress, the ways they movement from identify to identify, the devices they carry, and the fact that most people will outlive by decades the average man born before the last century. So ubiquitous are these scientific achievements that it is piece of cake to forget that in that location was zilch inevitable almost humanity’south ability to attain them.

Scientific progress is made when the drive to sympathise and command the earth is guided by a set of core principles and scientific methods. While challenges to previous scientific results may force researchers to examine their own practices and methods, the core principles and assumptions underlying scientific inquiry remain unchanged. In this context, the consideration of reproducibility and replicability in science is intended to maintain and heighten the integrity of scientific knowledge.

CORE PRINCIPLES AND ASSUMPTIONS OF SCIENTIFIC Enquiry

Science is inherently forward thinking, seeking to discover unknown phenomena, increment understanding of the globe, and answer new questions. As new knowledge is found, earlier ideas and theories may need to be revised. The core principles and assumptions of scientific inquiry cover this tension, allowing scientific discipline to progress while constantly testing, checking, and updating existing cognition. In this section, nosotros explore five core principles and assumptions underlying science:

1.

Nature is not capricious.

ii.

Knowledge grows through exploration of the limits of existing rules and mutually reinforcing evidence.

3.

Scientific discipline is a communal enterprise.

four.

Science aims for refined degrees of confidence, rather than consummate certainty.

5.

Scientific knowledge is durable and mutable.

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Nature Is Non Capricious

A bones premise of scientific inquiry is that nature is not capricious. “Science . . . assumes that the universe is, as its proper noun implies, a vast unmarried system in which the basic rules are everywhere the same. Knowledge gained from studying i role of the universe is applicative to other parts” (Rutherford and Ahlgren, 1991, p. 5). In other words, scientists assume that if a new experiment is carried out under the same conditions equally another experiment, the results should replicate. In March 1989, the electrochemists Martin Fleischmann and Stanley Pons claimed to have accomplished the fusion of hydrogen into helium at room temperature (i.e., “cold fusion”). In an example of science’south capacity for self-correction, dozens of laboratories attempted to replicate the event over the next several months. A consensus soon emerged inside the scientific customs that Fleischmann and Pons had erred and had non in fact achieved cold fusion.

Imagine a fictional history, in which the researchers responded to the charge that their original claim was mistaken, as follows: “While we are of form disappointed at the failure of our results to be replicated in other laboratories, this failure does nothing to prove that nosotros did not accomplish common cold fusion in our own experiment, exactly equally nosotros reported. Rather, what information technology demonstrates is that the laws of physics or chemistry, on the occasion of our experiment (i.e., in that particular identify, at that particular time), behaved in such a way as to allow for the generation of cold fusion. More exactly, it is our contention that the bones laws of physics and chemistry operate ane manner in those regions of infinite and time exterior of the location of our experiment, and some other way within that location.”

It goes without saying that this would be absurd. But why, exactly? Why, that is, should scientists not take seriously the fictional explanation higher up? The cursory answer, sufficient for our purposes, is that scientific inquiry (indeed, nigh any sort of inquiry) would grind to a halt if 1 took seriously the possibility that nature is
capricious
in the way it would have to be for this fictional caption to exist credible. Scientific discipline operates nether a standing presumption that nature follows rules that are
consistent, notwithstanding subtle, intricate, and challenging to discern they may be. In some systems, these rules are consistent across space and time—for instance, a physics study should replicate in different countries and in dissimilar centuries (assuming that differences in applicable factors, such as top or temperature, are deemed for). In other systems, the rules may be limited to specific places or times; for case, a rule of human behavior that is true in one country and one time period may not exist true in a different time and place. In upshot, all scientific disciplines seek to find rules that are true beyond the specific context within which they are discovered.

Cognition Grows Through Exploration of the Limits of Existing Rules and Mutually Reinforcing Testify

Scientists seek to discover rules about relationships or phenomena that exist in nature, and ultimately they seek to draw, explain, and predict. Because nature is non arbitrary, scientists presume that these rules will remain true as long as the context is equivalent. And because knowledge grows through bear witness nigh new relationships, researchers may find information technology useful to ask the same scientific questions using new methods and in new contexts, to decide whether and how those relationships persist or change. Most scientists seek to discover rules that are not only true in one specific context but that are besides confirmable by other scientists and are generalizable—that is rules that remain truthful fifty-fifty if the context of a separate study is not entirely the same as the original. Scientists thus seek to generalize their results and to discover the limits of proposed rules. These limits can often exist a rich source of new cognition nigh the system under study. For example, if a particular human relationship was observed in an older group but not a younger grouping, this suggests that the human relationship may be afflicted by age, cohort, or other attributes that distinguish the groups and may point the researcher toward farther inquiry.

Science Is a Communal Enterprise

Robert Merton (1973) described mod science as an establishment of “communalism, universalism, equity, and organized skepticism.” Science is an ongoing, communal conversation and a articulation trouble-solving enterprise that can include false starts and blind alleys, especially when taking risks in the quest to find answers to important questions. Scientists build on their own inquiry as well as the piece of work of their peers, and this building can sometimes bridge generations. Scientists today still rely on the work of Newton, Darwin, and others from centuries past.

Researchers have to exist able to empathize others’ research in guild to build on information technology. When research is communicated with clear, specific, and complete accounting of the materials and methods used, the results found, and the uncertainty associated with the results, other scientists can know how to interpret the results. The communal enterprise of science allows scientists to build on others’ piece of work, develop the necessary skills to deport high quality studies, and check results and confirm, dispute, or refine them.

Scientific results should be subject to checking by peers, and whatever scientist competent to perform such checking has the standing to practise and so. Confirming the results of others, for case, by replicating the results, serves equally ane of several checks on the processes by which researchers produce cognition. The original and replicated results are ideally obtained post-obit well-recognized scientific approaches within a given field of science, including collection of evidence and characterization of the associated sources and magnitude of uncertainties. Indeed, without understanding uncertainties associated with a scientific result (every bit discussed throughout this study), information technology is difficult to assess whether or not information technology has been replicated.

Science Aims for Refined Degrees of Confidence, Rather Than Complete Certainty

Uncertainty is inherent in all scientific knowledge, and many types of uncertainty can touch the reliability of a scientific effect. It is of import that researchers empathize and communicate potential sources of uncertainty in whatever system under study. Decision makers looking to use study results need to exist able to understand the uncertainties associated with those results. Understanding the nature of uncertainty associated with an analysis can help inform the option and use of quantitative measures for characterizing the results (meet Box 2-ane). At any stage of growing scientific sophistication, the aim is both to acquire what science tin can now reveal about the globe and to recognize the caste of doubtfulness attached to that knowledge.

Scientific Knowledge Is Durable and Mutable

As researchers explore the world through new scientific studies and observations, new prove may challenge existing and well-known theories. The scientific process allows for the consideration of new bear witness that, if apparent, may consequence in revisions or changes to current understanding. Testing of existing models and theories through the drove of new information is useful in establishing their strength and their limits (i.e., generalizability), and it ultimately expands homo knowledge. Such alter is inevitable as scientists develop improve methods for measuring and observing the world. The advent of new scientific noesis that displaces or reframes previous noesis should not be interpreted as a weakness in science. Scientific noesis is built on previous studies and tested theories, and the progression is ofttimes not linear. Science is engaged in a continuous procedure of refinement to uncover always-closer approximations to the truth.

CONCLUSION two-i: The scientific enterprise depends on the power of the scientific customs to scrutinize scientific claims and to gain confidence over fourth dimension in results and inferences that have stood up to repeated testing. Reporting of uncertainties in scientific results is a central tenet of the scientific procedure. It is incumbent on scientists to convey the appropriate caste of uncertainty in reporting their claims.

STATISTICAL INFERENCE AND HYPOTHESIS TESTING

Many scientific studies seek to measure, explain, and make predictions about natural phenomena. Other studies seek to notice and measure out the furnishings of an intervention on a organisation. Statistical inference provides a conceptual and computational framework for addressing the scientific questions in each setting.
Estimation
and
hypothesis testing
are wide groupings of inferential procedures. Estimation is suitable for settings in which the main goal is the assessment of the magnitude of a quantity, such every bit a measure out of a physical constant or the rate of change in a response corresponding to a change in an explanatory variable. Hypothesis testing is suitable for settings in which scientific interest is focused on the possible event of a natural event or intentional intervention, and a study is conducted to assess the evidence for and against this event. In this context, hypothesis testing helps answer binary questions. For example, will a plant grow faster with fertilizer A or fertilizer B? Do children in smaller classes larn more? Does an experimental drug work better than a placebo? Several types of more specialized statistical methods are used in scientific inquiry, including methods for designing studies and methods for developing and evaluating prediction algorithms.

Considering hypothesis testing has been involved in a major portion of reproducibility and replicability assessments, we consider this mode of statistical inference in some item. However, considerations of reproducibility and replicability apply broadly to other modes and types of statistical inference. For case, the upshot of drawing multiple statistical inferences from the same data is relevant for all hypothesis testing and in interpretation.

Studies involving hypothesis testing typically involve many factors that can innovate variation in the results. Some of these factors are recognized, and some are unrecognized. Random assignment of subjects or test objects to one or the other of the comparison groups is i manner to command for the possible influence of both unrecognized and recognized sources of variation. Random assignment may aid avoid systematic differences betwixt groups being compared, but it does not affect the variation inherent in the system (e.g., population or an intervention) under study.

Scientists utilise the term null hypothesis to describe the supposition that at that place is no difference between the two intervention groups or no issue of a treatment on some measured issue (Fisher, 1935). A commonly used formulation of hypothesis testing is based on the answer to the following question: If the null hypothesis is true, what is the probability of obtaining a difference at least every bit large as the observed 1? In general, the greater the observed divergence, the smaller the probability that a difference at least every bit large every bit the observed would be obtained when the zip hypothesis is truthful. This probability of obtaining a departure at least as big equally the observed when the null hypothesis is true is chosen the “p-value.”
iii

As traditionally interpreted, if a calculated
p-value is smaller than a defined threshold, the results may be considered statistically pregnant. A typical threshold may be
p
≤ 0.05 or, more stringently,
p
≤ 0.01 or
p
≤ 0.005.
4

In a statement issued in 2016, the American Statistical Association Board (Wasserstein and Lazar, 2016, p. 129) noted:

While the
p-value can be a useful statistical mensurate, it is commonly misused and misinterpreted. This has led to some scientific journals discouraging the use of
p-values, and some scientists and statisticians recommending their abandonment, with some arguments substantially unchanged since
p-values were offset introduced.

More than recently, it has been argued that
p-values, properly calculated and understood, can be informative and useful; however, a conclusion of statistical significance based on an arbitrary threshold of likelihood (fifty-fifty a familiar one such equally
p
≤ 0.05) is unhelpful and ofttimes misleading (Wasserstein et al., 2019; Amrhein et al., 2019b).

Agreement what a
p-value does not represent is as important as understanding what it does indicate. In particular, the
p-value does
not
stand for the probability that the aught hypothesis is true. Rather, the
p-value is calculated on the
assumption
that the zip hypothesis is truthful. The probability that the null hypothesis is true, or that the culling hypothesis is truthful, can be based on calculations informed in part by the observed results, simply this is not the aforementioned as a
p-value.

In scientific enquiry involving hypotheses most the effects of an intervention, researchers seek to avert two types of error that tin can lead to non-replicability:

  • Type I mistake—a fake positive or a rejection of the cypher hypothesis when it is correct

  • Type II error—a imitation negative or failure to reject a false null hypothesis, assuasive the nothing hypothesis to stand up when an alternative hypothesis, and not the zilch hypothesis, is right

Ideally, both Type I and Type Ii errors would be simultaneously reduced in research. For example, increasing the statistical power of a written report by increasing the number of subjects in a study can reduce the likelihood of a Type Ii error for whatever given likelihood of Type I fault.
5

Although the increase in information that comes with higher powered studies tin can help reduce both Type I and Type II errors, adding more subjects typically ways more time and cost for a study.

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Researchers are often forced to brand tradeoffs in which reducing the likelihood of one type of fault increases the likelihood of the other. For case, when
p-values are deemed useful, Type I errors may be minimized by lowering the significance threshold to a more stringent level (e.g., by lowering the standard
p
≤ 0.05 to
p
≤ 0.005). However, this would simultaneously increase the likelihood of a Blazon Ii error. In some cases, it may be useful to define dissever interpretive zones, where
p-values above one significance threshold are not accounted significant,
p-values below a more stringent significance threshold are deemed significant, and
p-values between the two thresholds are accounted inconclusive. Alternatively, one could simply accept the calculated
p-value for what it is—the probability of obtaining the observed result or one more extreme if the null hypothesis were true—and refrain from further interpreting the results as “significant” or “not pregnant.” The traditional reliance on a single threshold to determine significance tin can incentivize behaviors that work against scientific progress (run across the Publication Bias section in Affiliate 5).

Tension can arise between replicability and discovery, specifically, between the replicability and the novelty of the results. Hypotheses with depression
a priori
probabilities are less likely to be replicated. In this vein, Wilson and Wixted (2018) illustrated how fields that are investigating potentially ground-breaking results will produce results that are less replicable, on average, than fields that are investigating highly likely, nigh-established results. Indeed, a field could achieve nearly-perfect replicability if it express its investigations to prosaic phenomena that were already well known. As Wilson and Wixted (2018, p. 193) state, “We tin imagine pages full of findings that people are hungry after missing a repast or that people are sleepy after staying up all night,” which would not exist very helpful “for advancing understanding of the earth.” In the same vein, it would not be helpful for a field to focus solely on improbable, outlandish hypotheses.

The goal of science is non, and ought not to be, for all results to be replicable. Reports of non-replication of results tin generate excitement as they may indicate possibly new phenomena and expansion of electric current noesis. Also, some level of not-replicability is expected when scientists are studying new phenomena that are not well established. As knowledge of a organisation or phenomenon improves, replicability of studies of that detail system or phenomenon would be expected to increment.

Assessing the probability that a hypothesis is correct in part based on the observed results can also be approached through Bayesian analysis. This approach starts with
a priori
(before data observation) assumptions, known equally prior probabilities, and revises them on the basis of the observed information using Bayes’ theorem, sometimes described as the Bayes formula.

Appendix D illustrates how a Bayesian approach to inference can, under certain assumptions on the data generation mechanism and on the
a priori
likelihood of the hypothesis, use observed information to estimate the probability that a hypothesis is correct. I of the most striking lessons from Bayesian analysis is the profound effect that the pre-experimental odds take on the post-experimental odds. For example, nether the assumptions shown in Appendix D, if the prior probability of an experimental hypothesis was only 1 percent and the obtained results were statistically significant at the
p
≤ 0.01 level, just near one in eight of such conclusions that the hypothesis was true would be right. If the prior probability was every bit loftier equally 25 percent, then more than four of five such studies would exist deemed right. Every bit mutual sense would dictate and Bayesian analysis can quantify, it is prudent to adopt a lower level of confidence in the results of a written report with a highly unexpected and surprising result than in a report for which the results were
a priori
more than plausible (e.g., see Box two-ii).

BOX ii-two

Pre-Experimental Probability: An Case.

Highly surprising results may stand for an important scientific breakthrough, fifty-fifty though it is probable that just a minority of them may turn out over time to be correct. Information technology may exist crucial, in terms of the example in the previous paragraph, to learn which of the eight highly unexpected (prior probability, ane%) results can exist verified and which one of the five moderately unexpected (prior probability, 25%) results should exist discounted.

Keeping the idea of prior probability in heed, research focused on making small advances to existing cognition would result in a high replication rate (i.e., a loftier charge per unit of successful replications) because researchers would be looking for results that are very likely right. Only doing so would have the undesirable issue of reducing the likelihood of making major new discoveries (Wilson and Wixted, 2018). Many of import advances in science accept resulted from a bolder approach based on more speculative hypotheses, although this path likewise leads to dead ends and to insights that seem promising at first but fail to survive after repeated testing.

The “rubber” and “assuming” approaches to scientific discipline have complementary advantages. 1 might debate that a field has become too bourgeois if all attempts to replicate results are successful, but information technology is reasonable to expect that researchers follow up on new but uncertain discoveries with replication studies to sort out which promising results bear witness correct. Scientists should exist cognizant of the level of dubiety inherent in speculative hypotheses and in surprising results in any single study.

1

Many unlike definitions of “science” exist. In line with the committee’due south task, nosotros aim for this description to apply to a wide variety of scientific and engineering studies.

2
3

Text modified December 2019. In discussions related to the
p-value, the original written report used “likelihood” rather than “probability” and failed to notation that the
p-value includes the observed “and more extreme” results (Run across Section 3.2, Principles of Statistical Inference, Cox, 2006). Although the words probability and likelihood are interchangeable in everyday English, they are distinguished in technical usage in statistics.

4

The threshold for statistical significance is frequently referred to as
p
“less than” 0.05; we refer to this threshold as “less than or equal to.”

5

Statistical power is the probability that a test volition reject the goose egg hypothesis when a specific alternative hypothesis is true.

How Does the Field of Science Gain Knowledge and Understanding

Source: https://www.ncbi.nlm.nih.gov/books/NBK547541/

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