Confidence intervals rather than P values: estimation rather than hypothesis testing.

TLDR

Overemphasis on hypothesis testing and P‑values has obscured more useful approaches such as estimation and confidence intervals, which provide a range of plausible population values for the size of differences between groups. The paper presents methods for calculating confidence intervals for means, differences between means, and proportions, and offers suggestions for graphical display. When appropriate, confidence intervals should be reported for major findings in both the main text and the abstract.

Abstract

Overemphasis on hypothesis testing--and the use of P values to dichotomise significant or non-significant results--has detracted from more useful approaches to interpreting study results, such as estimation and confidence intervals. In medical studies investigators are usually interested in determining the size of difference of a measured outcome between groups, rather than a simple indication of whether or not it is statistically significant. Confidence intervals present a range of values, on the basis of the sample data, in which the population value for such a difference may lie. Some methods of calculating confidence intervals for means and differences between means are given, with similar information for proportions. The paper also gives suggestions for graphical display. Confidence intervals, if appropriate to the type of study, should be used for major findings in both the main text of a paper and its abstract.