![]() ![]() 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
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