


























We draw attention to one specific issue raised by Ioannidis (2005), that of very many hypotheses being tested in a given field of investigation. To better isolate the problem that arises in this (massive) multiple testing scenario, we consider a utopian setting where the hypotheses are tested with no additional bias. We show that, as the number of hypotheses being tested becomes much larger than the discoveries to be made, it becomes impossible to reliably identify true discoveries. This phenomenon, well-known to statisticians working in the field of multiple testing, puts in jeopardy any naive pursuit in (pure) discovery science.
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。