Understanding statistical significance.
BACKGROUND
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.


OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Hayat
Matthew J
MJ
School of Nursing, Johns Hopkins University, Baltimore, Maryland 21205, USA. mhayat2@son.jhmi.edu
D015982:Bias; D056808:Biostatistics; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D055317:Evidence-Based Practice; D017408:Guidelines as Topic; D006801:Humans; D007600:Judgment; D015399:Nursing Research / Q000379:methods; D017063:Outcome Assessment, Health Care; D011336:Probability; D012107:Research Design / Q000592:standards; D018401:Sample Size