Statistical significance is often misinterpreted as proof or scientific evidence of importance. This article addresses the most common statistical reporting error in the biomedical literature, namely, confusing statistical significance with clinical importance.
The aim of this study was to clarify the confusion between statistical significance and clinical importance by providing a historical perspective of significance testing, presenting a correct understanding of the information given by p values and significance testing, and offering recommendations for the correct use and reporting of statistical results.
The correct interpretation of p values and statistical significance is given, and the recommendations provided include a description of the current recommended guidelines for statistical reporting of the size of an effect.
This article provides a comprehensive overview of p values and significance testing and an understanding of the need for measures of importance and magnitude in statistical reporting.
Statistical significance is not an objective measure and does not provide an escape from the requirement for the researcher to think carefully and judge the clinical and practical importance of a study's results.
Matthew J. Hayat, PhD, is Assistant Professor and Biostatistician, School of Nursing, Johns Hopkins University, Baltimore, Maryland.
Accepted for publication: November 19, 2009.
The author thanks Gayle Page, DNSc, RN, FAAN, Jerilyn Allen, ScD, RN, FAAN, and Lynn D. Torbeck, MS, for their thoughtful reviews.
Corresponding author: Matthew J. Hayat, PhD, School of Nursing, Johns Hopkins University, 525 N. Wolfe St., Room 532, Baltimore, MD 21205 (e-mail: firstname.lastname@example.org).