4.6 Article

MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction

期刊

PLOS ONE
卷 16, 期 6, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0245824

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资金

  1. Higher Education Institutional Excellence Programme of the Ministry for Innovation and Technology in Hungary [2020-4.1.1.-TKP2020]
  2. ELIXIR Hungary
  3. company A5 Genetics Ltd, Hungary
  4. [2018-2.1.17-TET-KR-00001]
  5. [2020-1.1.6-JOVO. -2021-00013]
  6. [2018-1.3.1-VKE-2018-00032]

向作者/读者索取更多资源

Scientists across disciplines face the challenge of evaluating multiple hypotheses simultaneously, which requires consideration of statistical testing and confidence measures. Various strategies exist to address the issue of multiple hypothesis testing, with one approach being the use of multiple-testing correction methods.
Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. Several strategies exist to overcome the problem of multiple hypothesis testing. We aim to summarize critical statistical concepts and widely used correction approaches while also draw attention to frequently misinterpreted notions of statistical inference. We provide a step-by-step description of each multiple-testing correction method with clear examples and present an easy-to-follow guide for selecting the most suitable correction technique. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, allows to calculate False Discovery Rates (FDR) and q-values. The current summary provides a much needed practical synthesis of basic statistical concepts regarding multiple hypothesis testing in a comprehensible language with well-illustrated examples. The web tool will fill the gap for life science researchers by providing a user-friendly substitute for command-line alternatives.

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