The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results.
Although there is a growing understanding of the importance of statistical power considerations when designing studies and of the value of confidence intervals when interpreting data, confusion exists about the reverse arrangement: the role of confidence intervals in study design and of power in interpretation. Confidence intervals should play an important role when setting sample size, and power should play no role once the data have been collected, but exactly the opposite procedure is widely practiced. In this commentary, we present the reasons why the calculation of power after a study is over is inappropriate and how confidence intervals can be used during both study design and study interpretation.
Goodman
S N
SN
Johns Hopkins University School of Medicine, Baltimore, Maryland.
Berlin
J A
JA
D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D012107:Research Design