Interpreting p values and interval estimates based on practical relevance: guidance for the sports medicine clinician

Statistical methods are employed in medical research to estimate effects of treatments or health conditions across populations.1 2 This paper presents a framework to avoid common misinterpretations that undermine clinical decision-making.1 3 A convention in scientific articles is to report three statistical results: a point estimate, a p value and an interval estimate usually called ‘CI’.2 3 A point estimate is a value computed from the data that represents the ‘best guess’ about the studied effect; for example, the mean recovery time in a treatment group is the ‘best guess’ about the mean recovery time in the whole population receiving the treatment. The p value ‘p’ is a number between 0 and 1 used to assess the compatibility —also known as the consistency, consonance or agreement—between the data and a hypothesis about the effect (eg, the null hypothesis of zero treatment effect). The interval estimate is the range of the guesses about the effect with which the observed data are reasonably compatible, according to the method used to compute the interval.3–9 This editorial aims to guide the interpretation of p values and interval estimates for the sports medicine clinician. We present a series of recommendations for interpreting statistics through the compatibility viewpoint. Compatibility refers to how well the hypothesis fits or explains the data; compatibility is highest when p=1 and decreases as p approaches 0.3–6 10 Interpreting p values as measures of compatibility is an alternative to the traditional ‘significance’ interpretation; indeed, the latter is often misunderstood as referring to clinical significance and requires strong assumptions to justify its use for decisions.1 3–6 The minimum for reasonable compatibility is typically set at p=0.05, yielding a 100×(1–0.05)%=95% compatibility interval that is numerically identical to a ‘95% CI’ and so is also denoted as ‘95% CI’. Box …