BASIC STATISTICS FOR CLINICIAN .1. HYPOTHESIS-TESTING

In the first of a series of four articles the authors explain the stat istical concepts of hypothesis testing and p values. In many clinical trials investigators test a null hypothesis that there is no differenc e between a new treatment and a placebo or between two treatments. The result of a single experiment will almost always show some difference between the experimental and the control groups. Is the difference du e to chance, or is it large enough to reject the null hypothesis and c onclude that there is a true difference in treatment effects! Statisti cal tests yield a p value: the probability that the experiment would s how a difference as great or greater than that observed if the null hy pothesis were true. By convention, p values of less than 0.05 are cons idered statistically significant, and investigators conclude that ther e is a real difference. However, the smaller the sample size, the grea ter the chance of erroneously concluding that the experimental treatme nt does not. differ from the control - in statistical terms, the power of the test may be inadequate. Tests of several outcomes From one set of data may lead to an erroneous conclusion that an outcome is signif icant ii the joint probability of the outcomes is not taken into accou nt. Hypothesis testing has limitations, which will be discussed in the next article in the series.