In part 1 we discussed a 2005 paper by Ionnidis entitled "Why Most Published Research Findings Are False" [Full Text] [PMID: 16060722] that shows that there exist reasonable assumptions that could imply that most studies are false. That paper is the most downloaded paper from the PLoS Medicine journal. Here we discuss a 2007 paper by Goodman and Greenland who further illuminate the one by Ioannidis. They basically agree with Ionnidis pointing out that the argument is based on three basic components:
- that more than half the hypotheses that scientists consider are false, i.e. the prior probability is less than half.
- the division of studies into ones that are significant at the 5% level and ones that are not together with significance seeking on the part of researchers
- use of a mathematical argument (Bayes theorem) to show that with a low prior probability and weak evidence that the likelihood of the studies being correct is less than half (i.e. the posterior probability is less than half).