The p-value serves as a measure of statistical evidence against the null hypothesis, indicating how likely the observed results would be if there were truly no treatment effect. A small p-value suggests that the observed data are unlikely under the null hypothesis, providing evidence in favor of a treatment effect. By convention, p-values below 0.05 are typically considered statistically significant, though this threshold is arbitrary and should be interpreted in context with effect sizes, confidence intervals, and clinical relevance.

Proper interpretation of p-values requires understanding what they do and do not represent. The p-value is not the probability that the null hypothesis is true, nor is it the probability that the observed result occurred by chance alone. Rather, it represents the probability of obtaining results as extreme or more extreme than those observed, calculated under the assumption that the null hypothesis is true. A p-value of 0.03 means there is a 3% probability of seeing results this extreme if no treatment effect exists, not that there is a 3% probability that the treatment does not work.

Clinical trials typically pre-specify the significance level, often 0.05, which defines the threshold for declaring statistical significance. When p-values fall below this threshold, the null hypothesis is rejected in favor of the alternative hypothesis of a treatment effect. However, statistical significance should not be conflated with clinical significance; a treatment may produce statistically significant but clinically trivial effects, particularly in large trials with high precision. Conversely, clinically meaningful effects may fail to reach statistical significance in underpowered studies.