P-Value
The probability that an observed difference between groups in a clinical trial would occur if there were truly no difference. A p-value below 0.05 is the conventional threshold for statistical significance. Lower p-values indicate stronger evidence against the null hypothesis of no treatment effect.
Technical Context
P-values are calculated from the test statistic (t-test, chi-square, log-rank test, etc.) and the assumed null distribution. Common misconceptions: p<0.05 does NOT mean the probability that the result is true is 95%, nor that there is a 5% probability the null hypothesis is true. The p-value is conditional on the null hypothesis being true — it is the probability of observing data at least as extreme as what was observed, assuming no true difference exists. Bayesian approaches (calculating posterior probabilities of the treatment effect given the data) provide a more intuitive probability statement but require specifying prior beliefs. P-values are influenced by sample size — very large trials can produce small p-values for clinically trivial differences. Always evaluate p-values alongside effect size and confidence intervals.