〔Significance〕

The widespread use of Bonferroni correction encumbers the scientific process and wastes opportunities for discovery presented by big data,
because it discourages exploratory analyses by overpenalizing the total number of statistical tests performed. In this paper,
I introduce the harmonic mean p-value (HMP),
a simple to use and widely applicable alternative to Bonferroni correction motivated by Bayesian model averaging that greatly improves statistical power while maintaining control of the gold standard false positive rate.
The HMP has a range of desirable properties and offers a different way to think about large-scale exploratory data analysis in classical statistics.

※詳細は記事元で。

【記事元：PNAS(通称ピー・ナス)】
https://www.pnas.org/content/early/2019/01/08/1814092116