Scientific evidence

Last updated

Scientific evidence is evidence that serves to either support or counter a scientific theory or hypothesis, [1] although scientists also use evidence in other ways, such as when applying theories to practical problems. [2] Such evidence is expected to be empirical evidence and interpretable in accordance with the scientific method. Standards for scientific evidence vary according to the field of inquiry, but the strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls.[ citation needed ]

Contents

Principles of inference

A person's assumptions or beliefs about the relationship between observations and a hypothesis will affect whether that person takes the observations as evidence. [3] These assumptions or beliefs will also affect how a person utilizes the observations as evidence. For example, the Earth's apparent lack of motion may be taken as evidence for a geocentric cosmology. However, after sufficient evidence is presented for heliocentric cosmology and the apparent lack of motion is explained, the initial observation is strongly discounted as evidence.

When rational observers have different background beliefs, they may draw different conclusions from the same scientific evidence. For example, Priestley, working with phlogiston theory, explained his observations about the decomposition of mercuric oxide using phlogiston. In contrast, Lavoisier, developing the theory of elements, explained the same observations with reference to oxygen. [4] A causal relationship between the observations and hypothesis does not exist to cause the observation to be taken as evidence, [3] but rather the causal relationship is provided by the person seeking to establish observations as evidence.

A more formal method to characterize the effect of background beliefs is Bayesian inference. [5] In Bayesian inference, beliefs are expressed as percentages indicating one's confidence in them. One starts from an initial probability (a prior), and then updates that probability using Bayes' theorem after observing evidence. [6] As a result, two independent observers of the same event will rationally arrive at different conclusions if their priors (previous observations that are also relevant to the conclusion) differ. However, if they are allowed to communicate with each other, they will end in agreement (per Aumann's agreement theorem).

The importance of background beliefs in the determination of what observations are evidence can be illustrated using deductive reasoning, such as syllogisms. [7] If either of the propositions is not accepted as true, the conclusion will not be accepted either.

Utility of scientific evidence

Philosophers, such as Karl R. Popper, have provided influential theories of the scientific method within which scientific evidence plays a central role. [8] In summary, Popper provides that a scientist creatively develops a theory that may be falsified by testing the theory against evidence or known facts. Popper's theory presents an asymmetry in that evidence can prove a theory wrong, by establishing facts that are inconsistent with the theory. In contrast, evidence cannot prove a theory correct because other evidence, yet to be discovered, may exist that is inconsistent with the theory. [9]

Philosophical versus scientific views

In the 20th century, many philosophers investigated the logical relationship between evidence statements and hypotheses, whereas scientists tended to focus on how the data used for statistical inference are generated. [10] : S193  But according to philosopher Deborah Mayo, by the end of the 20th century philosophers had come to understand that "there are key features of scientific practice that are overlooked or misdescribed by all such logical accounts of evidence, whether hypothetico-deductive, Bayesian, or instantiationist". [10] : S194 

There were a variety of 20th-century philosophical approaches to decide whether an observation may be considered evidence; many of these focused on the relationship between the evidence and the hypothesis. In the 1950s, Rudolf Carnap recommended distinguishing such approaches into three categories: classificatory (whether the evidence confirms the hypothesis), comparative (whether the evidence supports a first hypothesis more than an alternative hypothesis) or quantitative (the degree to which the evidence supports a hypothesis). [11] A 1983 anthology edited by Peter Achinstein provided a concise presentation by prominent philosophers on scientific evidence, including Carl Hempel (on the logic of confirmation), R. B. Braithwaite (on the structure of a scientific system), Norwood Russell Hanson (on the logic of discovery), Nelson Goodman (of grue fame, on a theory of projection), Rudolf Carnap (on the concept of confirming evidence), Wesley C. Salmon (on confirmation and relevance), and Clark Glymour (on relevant evidence). [12] In 1990, William Bechtel provided four factors (clarity of the data, replication by others, consistency with results arrived at by alternative methods, and consistency with plausible theories of mechanisms) that biologists used to settle controversies about procedures and reliability of evidence. [13]

In 2001, Achinstein published his own book on the subject titled The Book of Evidence, in which, among other topics, he distinguished between four concepts of evidence: epistemic-situation evidence (evidence relative to a given epistemic situation), subjective evidence (considered to be evidence by a particular person at a particular time), veridical evidence (a good reason to believe that a hypothesis is true), and potential evidence (a good reason to believe that a hypothesis is highly probable). [14] Achinstein defined all his concepts of evidence in terms of potential evidence, since any other kind of evidence must at least be potential evidence, and he argued that scientists mainly seek veridical evidence but they also use the other concepts of evidence, which rely on a distinctive concept of probability, and Achinstein contrasted this concept of probability with previous probabilistic theories of evidence such as Bayesian, Carnapian, and frequentist. [14]

Simplicity is one common philosophical criterion for scientific theories. [15] Based on the philosophical assumption of the strong Church-Turing thesis, a mathematical criterion for evaluation of evidence has been conjectured, with the criterion having a resemblance to the idea of Occam's razor that the simplest comprehensive description of the evidence is most likely correct. [16] It states formally, "The ideal principle states that the prior probability associated with the hypothesis should be given by the algorithmic universal probability, and the sum of the log universal probability of the model plus the log of the probability of the data given the model should be minimized." [16] However, some philosophers (including Richard Boyd, Mario Bunge, John D. Norton, and Elliott Sober) have adopted a skeptical or deflationary view of the role of simplicity in science, arguing in various ways that its importance has been overemphasized. [17]

Emphasis on hypothesis testing as the essence of science is prevalent among both scientists and philosophers. [18] However, philosophers have noted that testing hypotheses by confronting them with new evidence does not account for all the ways that scientists use evidence. [2] For example, when Geiger and Marsden scattered alpha particles through thin gold foil, the resulting data enabled their experimental adviser, Ernest Rutherford, to very accurately calculate the mass and size of an atomic nucleus for the first time. [19] Rutherford used the data to develop a new atomic model, not only to test an existing hypothesis; such use of evidence to produce new hypotheses is sometimes called abduction (following C. S. Peirce). [19] Social-science methodologist Donald T. Campbell, who emphasized hypothesis testing throughout his career, later increasingly emphasized that the essence of science is "not experimentation per se" but instead the iterative competition of "plausible rival hypotheses", a process that at any given phase may start from evidence or may start from hypothesis. [20] Other scientists and philosophers have emphasized the central role of questions and problems in the use of data and hypotheses. [21]

Concept of scientific proof

While the phrase "scientific proof" is often used in the popular media, [22] many scientists and philosophers have argued that there is really no such thing as infallible proof. For example, Karl Popper once wrote that "In the empirical sciences, which alone can furnish us with information about the world we live in, proofs do not occur, if we mean by 'proof' an argument which establishes once and for ever the truth of a theory." [23] [24] Albert Einstein said:

The scientific theorist is not to be envied. For Nature, or more precisely experiment, is an inexorable and not very friendly judge of his work. It never says "Yes" to a theory. In the most favorable cases it says "Maybe", and in the great majority of cases simply "No". If an experiment agrees with a theory it means for the latter "Maybe", and if it does not agree it means "No". Probably every theory will someday experience its "No"—most theories, soon after conception. [25]

However, in contrast to the ideal of infallible proof, in practice theories may be said to be proved according to some standard of proof used in a given inquiry. [26] [27] In this limited sense, proof is the high degree of acceptance of a theory following a process of inquiry and critical evaluation according to the standards of a scientific community. [26] [27]

See also

Related Research Articles

Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.

<span class="mw-page-title-main">Falsifiability</span> Property of a theory/hypothesis/statement that can be logically contradicted by an empirical test

Falsifiability is a deductive standard of evaluation of scientific theories and hypotheses, introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934). A theory or hypothesis is falsifiable if it can be logically contradicted by an empirical test.

The word probability has been used in a variety of ways since it was first applied to the mathematical study of games of chance. Does probability measure the real, physical, tendency of something to occur, or is it a measure of how strongly one believes it will occur, or does it draw on both these elements? In answering such questions, mathematicians interpret the probability values of probability theory.

<span class="mw-page-title-main">Scientific method</span> Interplay between observation, experiment and theory in science

The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; the testability of hypotheses, experimental and the measurement-based statistical testing of deductions drawn from the hypotheses; and refinement of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters.

In philosophy, Occam's razor is the problem-solving principle that recommends searching for explanations constructed with the smallest possible set of elements. It is also known as the principle of parsimony or the law of parsimony. Attributed to William of Ockham, a 14th-century English philosopher and theologian, it is frequently cited as Entia non sunt multiplicanda praeter necessitatem, which translates as "Entities must not be multiplied beyond necessity", although Occam never used these exact words. Popularly, the principle is sometimes inaccurately paraphrased as "The simplest explanation is usually the best one."

<span class="mw-page-title-main">Philosophy of science</span> Study of foundations, methods, and implications of science

Philosophy of science is a branch of philosophy concerned with the foundations, methods, and implications of science. The central questions of this study concern what qualifies as science, the reliability of scientific theories, and the ultimate purpose of science. This discipline overlaps with metaphysics, ontology, and epistemology, for example, when it explores the relationship between science and truth. Philosophy of science focuses on metaphysical, epistemic and semantic aspects of science. Ethical issues such as bioethics and scientific misconduct are often considered ethics or science studies rather than the philosophy of science.

<span class="mw-page-title-main">Raven paradox</span> Paradox arising from the question of what constitutes evidence for a statement

The raven paradox, also known as Hempel's paradox, Hempel's ravens, or rarely the paradox of indoor ornithology, is a paradox arising from the question of what constitutes evidence for the truth of a statement. Observing objects that are neither black nor ravens may formally increase the likelihood that all ravens are black even though, intuitively, these observations are unrelated.

<span class="mw-page-title-main">Problem of induction</span> Question of whether inductive reasoning leads to definitive knowledge

First formulated by David Hume, the problem of induction questions our reasons for believing that the future will resemble the past, or more broadly it questions predictions about unobserved things based on previous observations. This inference from the observed to the unobserved is known as "inductive inferences", and Hume, while acknowledging that everyone does and must make such inferences, argued that there is no non-circular way to justify them, thereby undermining one of the Enlightenment pillars of rationality.

The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions.

Inductive reasoning is a method of reasoning in which a general principle is derived from a body of observations. It consists of making broad generalizations based on specific observations. Inductive reasoning is distinct from deductive reasoning, where the conclusion of a deductive argument is certain given the premises are correct; in contrast, the truth of the conclusion of an inductive argument is probable, based upon the evidence given.

Critical rationalism is an epistemological philosophy advanced by Karl Popper on the basis that, if a statement cannot be logically deduced, it might nevertheless be possible to logically falsify it. Following Hume, Popper rejected any inductive logic that is ampliative, i.e., any logic that can provide more knowledge than deductive logic. In other words if we cannot assert it logically, we should at the least try to logically falsify it, which led Popper to his falsifiability criterion.

Testability is a primary aspect of Science and the Scientific Method and is a property applying to an empirical hypothesis, involves two components:

  1. Falsifiability or defeasibility, which means that counterexamples to the hypothesis are logically possible.
  2. The practical feasibility of observing a reproducible series of such counterexamples if they do exist.
<span class="mw-page-title-main">Fallibilism</span> Philosophical principle

Originally, fallibilism is the philosophical principle that propositions can be accepted even though they cannot be conclusively proven or justified, or that neither knowledge nor belief is certain. The term was coined in the late nineteenth century by the American philosopher Charles Sanders Peirce, as a response to foundationalism. Theorists, following Austrian-British philosopher Karl Popper, may also refer to fallibilism as the notion that knowledge might turn out to be false. Furthermore, fallibilism is said to imply corrigibilism, the principle that propositions are open to revision. Fallibilism is often juxtaposed with infallibilism.

Verificationism, also known as the verification principle or the verifiability criterion of meaning, is the philosophical doctrine which asserts that a statement is meaningful only if it is either empirically verifiable or a truth of logic.

Inductivism is the traditional and still commonplace philosophy of scientific method to develop scientific theories. Inductivism aims to neutrally observe a domain, infer laws from examined cases—hence, inductive reasoning—and thus objectively discover the sole naturally true theory of the observed.

Statistics is a scientific field that deals with data. It utilizes data to solve practical problems and analyzes collected data in order to draw conclusions. Different approaches to analyzing the same data can lead to different conclusions. For example, weather forecasts can vary among different forecasting agencies. Conclusions drawn from statistical analysis often involve uncertainty as they represent the probability of an event occurring. For instance, a weather forecast indicating a 90% probability of rain would classify it as likely to rain, while a 5% probability would suggest it is unlikely to rain. The actual outcome, whether it rains or not, can only be determined afterwards.

Statistical proof is the rational demonstration of degree of certainty for a proposition, hypothesis or theory that is used to convince others subsequent to a statistical test of the supporting evidence and the types of inferences that can be drawn from the test scores. Statistical methods are used to increase the understanding of the facts and the proof demonstrates the validity and logic of inference with explicit reference to a hypothesis, the experimental data, the facts, the test, and the odds. Proof has two essential aims: the first is to convince and the second is to explain the proposition through peer and public review.

Evidence for a proposition is what supports the proposition. It is usually understood as an indication that the supported proposition is true. What role evidence plays and how it is conceived varies from field to field.

<span class="mw-page-title-main">Hypothesis</span> Proposed explanation for an observation, phenomenon, or scientific problem

A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research in a process beginning with an educated guess or thought.

References

  1. ↑ Taper, Mark L.; Lele, Subhash (2004). "The nature of scientific evidence: a forward-looking synthesis". In Taper, Mark L.; Lele, Subhash (eds.). The nature of scientific evidence: statistical, philosophical, and empirical considerations. Chicago: University of Chicago Press. pp. 527–551 (547). doi:10.7208/chicago/9780226789583.003.0016. ISBN   0226789551. OCLC   54461920. Scientific evidence is generally taken to be anything tending to refute or confirm a hypothesis.
  2. 1 2 Boyd, Nora Mills; Bogen, James (June 14, 2021). "Theory and observation in science". In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy . "Discussions about empirical evidence have tended to focus on epistemological questions regarding its role in theory testing ... even though empirical evidence also plays important and philosophically interesting roles in other areas including scientific discovery, the development of experimental tools and techniques, and the application of scientific theories to practical problems."
  3. 1 2 Longino, Helen (March 1979). Philosophy of Science, Vol. 46. pp. 37–42.
  4. ↑ Thomas S. Kuhn, The Structure of Scientific Revolution, 2nd Ed. (1970).
  5. ↑ William Talbott "Bayesian Epistemology" Accessed May 13, 2007.
  6. ↑ Thomas Kelly "Evidence". Accessed May 13, 2007.
  7. ↑ George Kenneth Stone, "Evidence in Science"(1966)
  8. ↑ Karl R. Popper,"The Logic of Scientific Discovery" (1959).
  9. ↑ Reference Manual on Scientific Evidence, 2nd Ed. (2000), p. 71. Accessed Feb 21, 2020. see: the 3rd edition of Reference Manual on Scientific Evidence
  10. 1 2 Mayo, Deborah G. (September 2000). "Experimental practice and an error statistical account of evidence". Philosophy of Science . 67 (Supplement): S193–S207. doi:10.1086/392819. JSTOR   188668. S2CID   61281250. A question regularly posed by scientists and philosophers of science is: When do empirical data provide a good test of, or reliable evidence for, a scientific hypothesis? Despite this shared interest, the considerations scientists appeal to in answering it are markedly different from those invoked in philosophical accounts of evidence and confirmation. Mayo's paper was part of the symposium "Evidence, data generation, and scientific practice: toward a reliabilist philosophy of experiment" at the 1998 biennial meetings of the Philosophy of Science Association. See also Achinstein's contribution to the symposium: Achinstein, Peter (2000). "Why philosophical theories of evidence are (and ought to be) ignored by scientists". Philosophy of Science . 67 (Supplement): S180–S192. doi:10.1086/392818. JSTOR   188667. S2CID   120774584.
  11. ↑ Carnap, Rudolf (1962) [1950]. Logical foundations of probability (2nd ed.). Chicago: University of Chicago Press. p.  462. ISBN   978-0226093437. OCLC   372957.
  12. ↑ Achinstein, Peter, ed. (1983). The concept of evidence . Oxford readings in philosophy. Oxford; New York: Oxford University Press. ISBN   0198750625.
  13. ↑ Bechtel, William (1990). "Scientific evidence: creating and evaluating experimental instruments and research techniques". PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association. 1990 (1): 559–572 (561). doi:10.1086/psaprocbienmeetp.1990.1.192732. JSTOR   192732. S2CID   62119768.
  14. 1 2 McArthur, Dan (August 2003). "Book review: Peter Achinstein, The book of evidence". Philosophy in Review . 23 (4): 235–237. Achinstein's four concepts are also summarized in: Achinstein, Peter (2014) [2008]. "Evidence". In Curd, Martin; Psillos, Stathis (eds.). The Routledge companion to philosophy of science. Routledge philosophy companions (2nd ed.). London; New York: Routledge. pp. 381–392. doi:10.4324/9780203744857. ISBN   9780415518741. OCLC   824535995.
  15. ↑ Baker, Alan (20 December 2016). "Simplicity". In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy .
  16. 1 2 Vitányi, Paul M. B.; Li, Ming (March 2000). "Minimum description length induction, Bayesianism, and Kolmogorov complexity" (PDF). IEEE Transactions on Information Theory . 46 (2): 446–464. arXiv: cs/9901014 . doi:10.1109/18.825807. S2CID   5971084. See also Chapter 5 in: Li, Ming; Vitányi, Paul (2019). An introduction to Kolmogorov complexity and its applications. Texts in computer science (4th ed.). Cham: Springer-Verlag. doi:10.1007/978-3-030-11298-1. ISBN   978-3030112974. OCLC   1106165074. S2CID   184483493.
  17. ↑ Fitzpatrick, Simon (2013). "Simplicity in the philosophy of science". Internet Encyclopedia of Philosophy . See especially Sections 1b and 4c.
  18. ↑ For example: Schindler, Samuel (2018). "Testability and non–ad hocness". Theoretical virtues in science: uncovering reality through theory. Cambridge, UK; New York: Cambridge University Press. pp. 6–7. doi:10.1017/9781108381352. ISBN   978-1108422260. No other criterion of a good scientific theory is as widely recognized as the falsifiability or testability of a theory—not only within the philosophy of science, but also way beyond it. And: "Understanding Science 101: Testing scientific ideas". undsci.berkeley.edu. University of California Museum of Paleontology. Testing hypotheses and theories is at the core of the process of science.
  19. 1 2 Thagard, Paul; Toombs, Ethan (2005). "Atoms, categorization and conceptual change" (PDF). In Cohen, Henri; Lefebvre, Claire (eds.). Handbook of categorization in cognitive science. Amsterdam: Elsevier. pp. 243–254 (253). doi:10.1016/B978-008044612-7/50065-2. ISBN   0080446124. OCLC   60667797. The features of abductive prototypes are hypothesized in order to explain observations, as when Rutherford inferred that the mass of an atom is concentrated in a very small region in order to explain why alpha particles pass through gold foil. Abductive prototypes can change dramatically when new data require revision of hypotheses concerning explanatory features. This is just what happened to the concept of an atom when the experiments of Thompson and Rutherford revealed the divisibility of atoms. Rutherford's interpretation of the Geiger–Marsden experiment is also mentioned as an example of abduction in: Faye, Jan (2014). "On interpretation". The nature of scientific thinking: on interpretation, explanation, and understanding. Houndmills, Basingstoke, Hampshire; New York: Palgrave Macmillan. pp. 60–84. doi:10.1057/9781137389831_3. ISBN   978-1137389824. OCLC   870285649.
  20. ↑ Campbell, Donald T. (1984) "Foreword". In Yin, Robert K. (2018) [1984]. Case study research: design and methods (6th ed.). Los Angeles: SAGE Publications. pp. xiii–xiv. ISBN   978-1506336169. OCLC   983826254. More and more I have come to the conclusion that the core of the scientific method is not experimentation per se but rather the strategy connoted by the phrase 'plausible rival hypotheses'. This strategy may start its puzzle solving with evidence, or it may start with hypothesis. Rather than presenting this hypothesis or evidence in the context-independent manner of positivistic confirmation (or even of postpositivistic corroboration), it is presented instead in extended networks of implications that (although never complete) are nonetheless crucial to its scientific evaluation. This strategy includes making explicit other implications of the hypothesis for other available data and reporting how these fit. It also includes seeking out rival explanations of the focal evidence and examining their plausibility. The plausibility of these rivals is usually reduced by ramification extinction, that is, by looking at their other implications on other data sets and seeing how well these fit. This idea is further discussed in several chapters in: Bickman, Leonard, ed. (2000). Donald Campbell's legacy. Thousand Oaks, CA: SAGE Publications. OCLC   42603382.
  21. ↑ For example: "Thematic issue: Scientific method as a problem-solving and question-answering technique: Vol. 47, No. 1 of Synthese". April 1981. One article from the issue is: Nickles, Thomas (April 1981). "What is a problem that we may solve it?". Synthese . 47 (1): 85–118. doi:10.1007/BF01064267. JSTOR   20115620. S2CID   46982553. Data sometimes do not constitute the problem (or the primary problem) but serve chiefly as evidence that a problem (or at least a deeper problem) exists. See also: Nickles, Thomas (1988). "Questioning and problems in philosophy of science: problem-solving versus directly truth-seeking epistemologies". In Meyer, Michel (ed.). Questions and questioning. Grundlagen der Kommunikation = Foundations of communication. Berlin; New York: De Gruyter. pp. 43–67. doi:10.1515/9783110864205.43. ISBN   3110106809. And from a scientist's perspective: Krauss, Lawrence M. (14 May 2015). "The big unanswered questions". The Huffington Post . Retrieved 15 May 2015.
  22. ↑ See, for example, "Greenpeace co-founder: No scientific proof humans are dominant cause of warming climate". Fox News Channel . 28 February 2014. Retrieved 19 March 2014.
  23. ↑ Popper, Karl (2011) [1966]. The Open Society and Its Enemies (5th ed.). Routledge. pp. 229–230. ISBN   978-1136700323.
  24. ↑ Theobald, Douglas (1999–2012). "29+ Evidences for Macroevolution". TalkOrigins Archive . Retrieved 19 March 2014.
  25. ↑ Gaither, Carl (2009). Gaither's Dictionary of Scientific Quotations . New York: Springer. p.  1602. ISBN   978-0-387-49575-0.
  26. 1 2 Walton, Douglas N.; Zhang, Nanning (May 2013). "The epistemology of scientific evidence". Artificial Intelligence and Law. 21 (2): 173–219 (214). doi:10.1007/s10506-012-9132-9. Traditional epistemology established knowledge on the basis of a false concept—true belief. On our theory, scientific evidence should be based on a process of justifying the agent's reasonable acceptance of a hypothesis in an inquiry that ends in proof. We have shown in section V how this procedure can be modeled using the Carneades Argumentation System. Any proposition that cannot be proved in an inquiry to an appropriate standard of proof following this kind of epistemological procedure is not acceptable as knowledge.
  27. 1 2 Walton, Douglas N. (2016). Argument evaluation and evidence. Law, governance and technology series. Vol. 23. Cham; New York: Springer-Verlag. p.  224. doi:10.1007/978-3-319-19626-8. ISBN   9783319196268. OCLC   919080389. To say that something is knowledge, it is important that the proposition claimed as knowledge be based on evidence of a kind that reaches a level where the proposition passes beyond the level of being accepted as true because it is based on evidence. Only when it is proved by a certain kind of evidence, that is sufficient for the discipline, or more generally the context in which the proposition was claimed, can something be properly said to be knowledge. The standard has to be high enough in a scientific inquiry to minimize the possibility that the proposition accepted as true will later have to be retracted.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.