Statistical reasoning in clinical trials: hypothesis testing.

Hypothesis testing is based on certain statistical and mathematical principles that allow investigators to evaluate data by making decisions based on the probability or implausibility of observing the results obtained. However, classic hypothesis testing has its limitations, and probabilities mathematically calculated are inextricably linked to sample size. Furthermore, the meaning of the p value frequently is misconstrued as indicating that the findings are also of clinical significance. Finally, hypothesis testing allows for four possible outcomes, two of which are errors that can lead to erroneous adoption of certain hypotheses: 1. The null hypothesis is rejected when, in fact, it is false. 2. The null hypothesis is rejected when, in fact, it is true (type I or alpha error). 3. The null hypothesis is conceded when, in fact, it is true. 4. The null hypothesis is conceded when, in fact, it is false (type II or beta error). The implications of these errors, their relation to sample size, the interpretation of negative trials, and strategies related to the planning of clinical trials will be explored in a future article in this journal.