Sample Size and Power in Psychiatric Research*

The conclusions drawn by study are susceptible to two types of errors. The more familiar one occurs when it is believed that there was a true difference between the groups or an association between two variables, when in fact this observation was due to chance (a Type I error). The second potential error consists of falsely concluding that there was no difference or association when indeed there was one (a Type II error). Most researchers know that the probability of a Type I error can be controlled by the setting of alpha level of statistical significance; however, many are unaware of methods to control or estimate Type II errors, based on estimates of an appropriate sample size. This paper discusses techniques researchers can use to calculate the sample sizes required for studies, and the effects of sample sizes which are too small or too large. If it is too small, there is an increased risk of a Type II error, whereas if it is too large, there may be a needless waste of time, money, and effort. The paper also discusses how readers of research articles can determine whether or not negative findings reported by a study are a true reflection of the lack of any difference between groups, or a result of insufficient sample size.