Statistical significance is the conclusion that a research result is unlikely to have occurred by chance alone. It is typically expressed as a p-value: the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true.

By convention, a p-value less than .05 is often treated as statistically significant, though the threshold is arbitrary and should be interpreted in context. Significance depends on both the size of the effect and the size of the sample. A tiny effect can reach significance in a very large sample; a substantial effect may fail to do so in a small one. Significance does not equal practical or theoretical importance.

Researchers are increasingly encouraged to report effect sizes and confidence intervals alongside p-values, and to avoid treating the .05 threshold as a bright line between truth and noise. Replication remains the ultimate test of whether a finding is dependable.

When a result is unlikely under the assumption of pure chance, it is called statistically significant.