Statistical Significance

In Detail

Statistical significance is a mathematical concept used in clinical trials to determine whether the observed difference between the treatment and control groups is likely to be real rather than due to random chance. The most common threshold for statistical significance is a p-value of less than 0.05, which means there is less than a 5% probability that the observed results would have occurred by chance if the treatment had no effect. When a trial achieves statistical significance on its primary endpoint, it provides strong evidence that the treatment has a genuine effect. However, statistical significance alone does not tell the whole story. A result can be statistically significant but clinically insignificant — meaning the effect is real but too small to matter in practice. For example, a blood pressure drug that lowers systolic pressure by 1 mmHg might achieve statistical significance in a large enough trial, but such a small reduction would have minimal impact on patient health. Conversely, a result that does not reach statistical significance may still represent a clinically meaningful trend, particularly in small studies that lack statistical power. Researchers also consider confidence intervals, effect sizes, and the number needed to treat (NNT) alongside p-values. The FDA evaluates both statistical significance and clinical meaningfulness when reviewing drug applications. Understanding statistical significance helps patients and caregivers interpret clinical trial results reported in the news — a "statistically significant" result means the finding is unlikely to be a fluke, but the magnitude of benefit still requires clinical context.