title: Explorations in statistics: statistical facets of reproducibility.
abstract: Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eleventh installment of Explorations in Statistics explores statistical facets of reproducibility. If we obtain an experimental result that is scientifically meaningful and statistically unusual, we would like to know that our result reflects a general biological phenomenon that another researcher could reproduce if (s)he repeated our experiment. But more often than not, we may learn this researcher cannot replicate our result. The National Institutes of Health and the Federation of American Societies for Experimental Biology have created training modules and outlined strategies to help improve the reproducibility of research. These particular approaches are necessary, but they are not sufficient. The principles of hypothesis testing and estimation are inherent to the notion of reproducibility in science. If we want to improve the reproducibility of our research, then we need to rethink how we apply fundamental concepts of statistics to our science.
authors.affiliation: Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado; and Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado EverettD@NJHealth.org.
authors.forename: Douglas
authors.initials: D
authors.lastname: Curran-Everett
mesh_terms: D035843:Biomedical Research / Q000592:standards*; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012108:Research Personnel / Q000592:standards*
keywords: estimation; hypothesis test; power; significance test