{"title":"Alternatives to the statistical mass confusion of testing for no-effect","authors":"Josh L. Morgan","doi":"arxiv-2407.07114","DOIUrl":null,"url":null,"abstract":"Statisticians and researchers have argued about the merits of effect size\nestimation relative to hypothesis testing for decades. Cell biology has largely\navoided this debate and is now in a quantitation crisis. In experimental cell\nbiology, statistical analysis has grown to mean testing the null hypothesis\nthat there was no experimental effect. This weak form of hypothesis testing\nneglects effect size, is universally misinterpreted, and is disastrously prone\nto error when combined with high-throughput cell biology. The first part of the\nsolution proposed here is to limit statistical hypothesis testing to the small\nsubset of experiments where a biologically meaningful null hypotheses can be\ndefined prior to the experiment. The second part of the solution is to make\nconfidence intervals the default statistic in cell biology.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.07114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Statisticians and researchers have argued about the merits of effect size
estimation relative to hypothesis testing for decades. Cell biology has largely
avoided this debate and is now in a quantitation crisis. In experimental cell
biology, statistical analysis has grown to mean testing the null hypothesis
that there was no experimental effect. This weak form of hypothesis testing
neglects effect size, is universally misinterpreted, and is disastrously prone
to error when combined with high-throughput cell biology. The first part of the
solution proposed here is to limit statistical hypothesis testing to the small
subset of experiments where a biologically meaningful null hypotheses can be
defined prior to the experiment. The second part of the solution is to make
confidence intervals the default statistic in cell biology.