![]() In many research fields, evidence from several tests and experiments has to be combined in order to reach a conclusion about a hypothesis. Research programs involve larger sets of hypotheses that are evaluated by different tests and complementary technical approaches. Scientific research is, however, typically more complex than accounted for by the approach outlined in Ioannidis' essay, where single tests are used to evaluate single hypotheses. These findings raise concerns about the reliability of published research in those fields of the life sciences that are characterized by low priors, error-prone tests, and considerable competition. The more often a hypothesis is tested independently, the more likely a positive result is obtained and published even if the hypothesis is false. Moreover, competition has been argued to have a negative effect on the reliability of research, because the same hypotheses are tested independently by competing research groups. Small effect sizes, error-prone tests, low priors of the tested hypothesis, and biases in the interpretation of research findings can lead to a large fraction of published false positives –. This is because findings tend to be evaluated by p-value rather then posterior probability, and because positive results are more likely to be published than negative results. Ioannidis, it has been argued that at least in some research fields, most of the published findings are false. In a recent controversial essay by J.P.A. For a given prior probability, test result and test statistics, the posterior probability of a hypothesis can be calculated using Bayes' Theorem. For example, a positive result on a very improbable hypothesis is likely a false positive, while a positive result on a more probable hypothesis is more likely to be true. the posterior probability, does not only depend on the test statistics, but also on the probability of the hypothesis before the test, i.e. The probability that a hypothesis is true after a test result has been obtained, i.e. The probability β of missing a true relation corresponds to the power of a test, 1-β. This type of error is referred to as type II error or ‘false negative’. Conversely, a test may fail to confirm a true hypothesis. The probability α of obtaining a positive result although the hypothesis is false relates to the significance level of a test. This type of error is commonly referred to as type I error or ‘false positive’. A test may give confirmation for a hypothesis that is actually false. The testing of scientific hypotheses is typically associated with two types of statistical errors. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. AD is supported by the Jan Wallander and Tom Hedelius Foundation. DR is supported by the National Science Foundation Graduate Research Fellowship Program. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: TP is supported by Society in Science - The Branco Weiss Fellowship. Received: JAccepted: JanuPublished: February 25, 2009Ĭopyright: © 2009 Pfeiffer et al. PLoS ONE 4(2):Įditor: Alan Ruttenberg, Science Commons, United States of America ![]() Citation: Pfeiffer T, Rand DG, Dreber A (2009) Decision-Making in Research Tasks with Sequential Testing.
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