title: What your statistician never told you about P-values.
abstract: We provide a non-technical overview of what P-values are and what they are not. To determine how P-values ought to be used, reported, and interpreted, we must first clarify the often-overlooked differences between, and proper usages of, significance testing and hypothesis testing. Several clinical examples are given to illustrate these differences, and failure to distinguish between them is seen to be problematic. Common misinterpretations of P-values are explained. Confidence intervals provide essential information where P-values are deficient in doing so and they therefore play an essential role in reporting and interpreting study results.
authors.affiliation: Division of Research in Women's Health and the George Anderson Outcomes Measurement Unit, Department of Obstetrics and Gynecology, WOmen and Infants Hospital, Brown University, Providence, Rhode Island 02912, USA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Blume
authors.forename: Jeffrey F
authors.initials: JF
authors.lastname: Peipert
mesh_terms: D002986:Clinical Trials as Topic / Q000592:standards / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D011336:Probability; D018401:Sample Size; D013223:Statistics as Topic