Evidence-appraisal glossary
Statistical significance
Statistical significance means a result is unlikely to have arisen by chance under the null hypothesis, usually judged by a p-value below a preset threshold such as 0.05. It signals that an effect was detected, but says nothing about how large, important, or real-world meaningful that effect is.
Also called: significant, p < 0.05.
Statistical significance is a decision rule: if the p-value falls below a chosen threshold (commonly 0.05), the result is labeled significant and the null hypothesis of no effect is set aside. It reflects the strength of evidence against chance, but it is heavily influenced by sample size. A very large study can make a tiny, unimportant difference statistically significant, while a small study can miss a genuinely large effect. When reading a study, do not stop at the word significant; look at the actual effect size and its confidence interval to judge whether the finding matters. For example, a weight-loss supplement tested in 50,000 people might show a statistically significant average loss of 0.2 pounds, which is real in a statistical sense but trivial in practice. Ask whether significance is being confused with importance, and whether the threshold was set before the data were seen.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.