[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-statistical-significance::en":3,"gloss-cluster-statistical-significance::en":23,"gloss-next-statistical-significance::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"statistical-significance","analytics","Statistical Significance","Statistical significance is a statement about how easily an observed difference could have arisen by chance if there were no real difference at all. The conventional test computes a p-value — the probability of seeing a result at least this extreme under that assumption — and calls the result significant when it falls below a threshold set in advance, usually 0.05. It is a guard against being fooled by noise, and it is routinely misread. Three misreadings are worth naming. A p-value is not the probability that your change works, and one above the threshold is not evidence that it does not — most often it means the sample was too small to tell. Significance is not size: with enough traffic, a difference far too small to matter commercially will clear any threshold, which is why an effect estimate with a confidence interval is more useful to a decision than a yes\u002Fno verdict. And significance applies to the test you planned, not the one you found — checking daily and stopping when the line crosses, or comparing twenty segments and reporting the one that cleared, produces false positives at a far higher rate than the threshold implies. The practical discipline is to fix the primary metric, the sample size and the duration before starting; to report the estimated effect and its interval rather than a bare verdict; and to treat segment-level findings as hypotheses for a future test rather than results. Repeated tests on the same data need an explicit correction, and a result that is significant but tiny is a reason to look at the cost of shipping it.","Statistical significance says how easily chance could explain a result — the three common misreadings, and why an effect size with an interval beats a verdict.",null,[11,14,17,20],{"slug":12,"name":13},"ab-testing","A\u002FB Testing",{"slug":15,"name":16},"conversion-rate-optimization","Conversion Rate Optimization (CRO)",{"slug":18,"name":19},"cross-validation","Cross-Validation",{"slug":21,"name":22},"product-analytics","Product Analytics",[24,26,29,33,36,39,42,45,48,51,54,57],{"slug":12,"category":5,"name":13,"updated_at":25},"2026-08-24T02:46:38+00:00",{"slug":27,"category":5,"name":28,"updated_at":25},"active-user","Active User (DAU, WAU, MAU)",{"slug":30,"category":5,"name":31,"updated_at":32},"autocapture","Autocapture","2026-08-24T02:46:37+00:00",{"slug":34,"category":5,"name":35,"updated_at":25},"cost-per-resolution","Cost per Resolution",{"slug":37,"category":5,"name":38,"updated_at":32},"customer-data-platform","Customer Data Platform (CDP)",{"slug":40,"category":5,"name":41,"updated_at":25},"deflection-rate","Deflection Rate",{"slug":43,"category":5,"name":44,"updated_at":25},"guardrail-metric","Guardrail Metric",{"slug":46,"category":5,"name":47,"updated_at":32},"identity-resolution","Identity Resolution",{"slug":49,"category":5,"name":50,"updated_at":32},"multi-touch-attribution","Multi-Touch Attribution",{"slug":52,"category":5,"name":53,"updated_at":25},"novelty-effect","Novelty Effect",{"slug":55,"category":5,"name":56,"updated_at":32},"retention-curve","Retention Curve",{"slug":58,"category":5,"name":59,"updated_at":25},"sample-ratio-mismatch","Sample Ratio Mismatch (SRM)"]