Signifikanssi

Signifikanssi, in statistics often translated as statistical significance, is a formal assessment used in hypothesis testing to determine whether an observed effect is unlikely to have occurred by chance under a null hypothesis. The assessment relies on a chosen significance level α, commonly 0.05.

A central tool is the p-value, the probability of obtaining data at least as extreme as the observed, assuming the null hypothesis is true. If p-value ≤ α, researchers reject the null in favor of an alternative. If p > α, the result is not considered statistically significant, though this does not prove the null true.

Limitations: statistical significance does not imply practical importance. It can be influenced by sample size; large samples can yield very small p-values for negligible effects, while small samples may fail to detect meaningful effects. Multiple testing increases false positives unless corrected (e.g., Bonferroni, Holm). P-hacking and flexible analysis choices can inflate significance.

Good practice combines p-values with effect sizes and confidence intervals; report measures of practical significance (e.g., Cohen's d, odds ratio) and the study's power. Consider preregistration and replication to establish robust conclusions. In addition to frequentist methods, Bayesian approaches (Bayes factors) offer different interpretations of evidence.

In Finnish usage, signifikanssi refers to tilastollinen merkitsevyys, the probability that an observed effect would be as extreme as observed under the null hypothesis, under a chosen α.