What Make Biology Unique? Cambridge Univ. Press, UK, Light marks and edgewear to jacket.
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What Makes Biology Unique?: Considerations on the Autonomy of a Scientific Discipline
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Scientific Objectivity (Stanford Encyclopedia of Philosophy)
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Our Day return guarantee still applies. Thus, Bayesianism provides not more than a partial answer to securing scientific objectivity from personal idiosyncrasy. On the other hand, the objections to the above proposals are no knockout arguments, and further developments of the discussed approaches may help to reconcile transparency about subjective assumptions with objectivity in interpreting statistical evidence.
That said, one may argue that the theories we discussed so far all miss the target. Bayesians primarily address the question of which theories we should rationally believe in. The decision procedures reviewed in section 3. Both analyze the concept of statistical evidence from the vantage point of their primary focus—beliefs and decisions. But can't we quantify the support for or against a hypothesis in an intersubjectively compelling way, without buying into a subjectivist or a behavioral framework?
This is the ambition of frequentist and likelihood-based explications of scientific evidence. The frequentist conception of evidence is based on the idea of the statistical test of a hypothesis. Under the influence of the statisticians Neyman and Pearson, tests were often regarded as rational decision procedures that minimize the relative frequency of wrong decisions in a hypothetical series of repetitions of a test. As we have seen in section 3.
Moreover, the losses associated with erroneously accepting or rejecting that hypothesis depend on the context of application which may be unbeknownst to the experimenter. This speaks for a division of labor where scientists restrict themselves to an evidential interpretation of statistical tests , and leave the actual decisions to policy-makers and regulatory agencies.
Such an approach has been developed by Ronald A.
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Fisher — , and it has become the orthodox solution to statistical inference problems. In other words, if a result has lower probability under the null hypothesis H than most other possible results, then it undermines the tenability of H :. Fisher Then, the strength of evidence against the tested hypothesis is equal to the p-value —the probability of obtaining a result that is as least as extreme as the actually observed data.
Figure 1 gives a graphical illustration. This probability measures how strongly E speaks against H , compared to other possible results, and the lower it is, the stronger the evidence against H. Conventionally, a p-value smaller than. This concept of evidence is apparently objective, but beset with a variety of problems see Sprenger for a detailed discussion. There is no intersubjectively compelling justification why this or any other particular standard of evidence should be used in order to quantify the concept of significance.
From an institutional point of view, the frequentist conception of p-values is problematic as well.
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What is more, even in the absence of a causal relation between two quantities, one may find a significant and therefore publishable result by pure chance. The probability that this happens by accident is equal to the statistical significance threshold i. Ioannidis therefore concludes that most published research findings are false —an effect partially due to the frequentist logic of evidence.
Indeed, researchers often fail to replicate findings by another scientific team, and periods of excitement and subsequent disappointment are not uncommon in frontier science. Finally, there is a principled philosophical objection against the objectivity of frequentist evidence: the sample space dependence.
That is, in frequentist statistics, the strength of the evidence depends on which results could have been observed but were not observed. For instance, the post-experimental assessment of the evidence has to be changed when we learn about a defect in our measurement instrument, even if that defect is not relevant for the range of the actually observed results! On a Bayesian reading, this implies that frequentist evidence statements depend on the intentions of the experimenter Edwards, Lindman and Savage ; Sprenger : Would she have continued the trial if the results had been different?
How would she have reacted to unforeseen circumstances? Freedom from personal bias seems hard to realize if one's inference depends on the answer to such questions. A middle ground between frequentist and Bayesian inference is provided by likelihoodist inference, based on Alan Turing and I. This is because the probabilities of the actual evidence E under the competing hypotheses are called the likelihoods of H on E.
Therefore, a minority of statisticians e.
However, the likelihoodist cannot use subjective probability in order to transform a composite hypothesis into a simple one. Summing up our findings, no statistical theory of evidence manages to eliminate all sources of personal bias and idiosyncrasy. The Bayesian is honest about it: she considers subjective assumptions to be ineliminable from scientific reasoning.
This does not rule out that constrastive aspects of statistical evidence may be quantified in an objective way, e. The frequentist conception based on p-values still dominates statistical practice, but it suffers from several conceptual drawbacks, and in particular the misleading impression of objectivity. This also has far-reaching implications for fields such as evidence-based medicine, where randomized controlled trials the most valuable source of evidence are typically interpreted in a frequentist way.
A defense of frequentist inference should, in our opinion, stress that the relatively rigid rules for interpreting statistical evidence facilitate communication and assessment of research results in the scientific community—something that is harder to achieve for a Bayesian. So far everything we discussed was meant to apply across all or at least most of the sciences. In this section we will look at a number of specific issues that arise in the social science, in economics, and in evidence-based medicine.
There is a long tradition in the philosophy of social science maintaining that there is a gulf in terms of both goals as well as methods between the natural and the social sciences. See also the entries on hermeneutics and Max Weber.