Scientific evidence has no universally accepted definition but generally refers to evidence which serves to either support or counter a scientific theory or hypothesis. Such evidence is generally expected to be empirical and properly documented in accordance with scientific method such as is applicable to the particular field of inquiry. Standards for evidence may vary according to whether the field of inquiry is among the natural sciences or social sciences . Evidence may involve understanding all steps of a process, or one or a few observations, or observation and statistical analysis of many samples without necessarily understanding the mechanism.
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Evidence is information, such as facts, coupled with principles of inference (the act or process of deriving a conclusion), that make information relevant to the support or negation of a hypothesis . Scientific evidence is evidence where the dependence of the evidence on principles of inference is not conceded, enabling others to examine the background beliefs or assumptions employed to determine if facts are relevant to the support of or falsification of a hypothesis.[1]
A person’s assumptions or beliefs about the relationship between alleged facts and a hypothesis will determine whether that person takes the facts as evidence.[1] Consider, for example alternative uses of the observation that day and night alternate at a steady rate. In an environment where the observer makes a causal connection between exposure to the sun and day, the observer may take the observation of day and night as evidence for a theory of cosmology. Without an assumption or belief that a causal connection exists between exposure to the sun and the observance of day, the observation of day will be discounted as evidence of a cosmological theory.
A person’s assumptions or beliefs about the relationship between alleged facts and a hypothesis will also determine how a person utilizes the facts as evidence. Continuing with the same example, in an environment where geocentric cosmology is prevalent, the observation of day and night may be taken as evidence that the sun moves about the earth. Alternatively, in an environment where heliocentric cosmology is prevalent, the same observation may be taken as evidence that the earth is spinning about an axis.[1] In summary, beliefs or assumptions about causal relationships are utilized to determine whether facts are evidence of a hypothesis.
Background beliefs differ. As a result, where observers operate under different paradigms, rational observers may find different meaning in scientific evidence from the same event.[2] For example, Priestley, working with phlogiston theory, took his observations about the decomposition of what we know today as mercuric oxide as evidence of the phlogiston. In contrast, Lavoisier, developing the theory of elements, took the same facts as evidence for oxygen.[3] Note that a causal relationship between the facts and hypothesis does not exist to cause the facts to be taken as evidence,[1] but rather the causal relationship is provided by the person seeking to establish facts as evidence.
A more formal method to characterize the effect of background beliefs is Bayesianism.[4] Bayesian theory provides that one’s beliefs depend on evidence to which one is exposed and one’s prior experiences (probability distribution, in Bayesian terms).[5] As a result, two observers of the same event will rationally arrive at different evidence, given the same facts, because their priors (previous experiences) differ.
The importance of background beliefs in the determination of what facts are evidence can be illustrated using as provided by Aristotle's syllogistic logic. A standard syllogism is a triad where two propositions jointly imply the conclusion[6]:
All men are mortal,
and
Aristotle is a man;
therefore
Aristotle is mortal.
If either of the propositions is not accepted as true, the conclusion will not be deemed to follow from them (it may or may not be true).
Philosophers, such as Karl R. Popper, have provided influential theories of the scientific method within which scientific evidence plays a central role.[7] In summary, Popper provides that a scientist creatively develops a theory which 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.[8] See falsificationism for more on this view of scientific evidence.
The Philosophic community has invested extensive resources to address logical requirements for scientific evidence by examination of the relationship between evidence and hypotheses, in contrast to scientific approaches which focus on the candidate facts and their context.[9] Bechtel, as an example of a scientific approach, provides factors (clarity of the data, replication by others, consistency with results arrived at by alternative methods and consistency with plausible theories) useful for determination if facts rise to the level of scientific evidence.[10]
A variety of philosophical approaches are available for the evaluation of evidence, many of which focus on the relationship between the evidence and the hypothesis, to determine if the facts rise to the level of evidence. Carnap recommends distinguishing such theories of evidence using three concepts: whether the theory is classificatory (does the evidence confirm the hypothesis), comparative (does the evidence support a first hypothesis more than an alternative hypothesis) or quantitative (the degree to which the evidence supports a hypothesis).[11] Achinstein provides a concise presentation by prominent philosophers on evidence, including Carl Hempel (Confirmation), Nelson Goodman (of grue fame), R. B. Braithwaite, Norwood Russell Hanson, Wesley C. Salmon, Clark Glymour and Rudolf Carnap[12]
Based on the philosophical assumption of the Strong Church-Turing Universe Thesis, a mathematical criterion for evaluation of evidence has been proven, 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. 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." [13]