How Does the Field of Science Gain Knowledge and Understanding

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.

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.

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).

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.