Merkittävyysanalyysi

Merkittävyysanalyysi is a statistical method used to determine whether observed differences in data are unlikely to have arisen by chance alone. The term originates from the Finnish language, where "merkittävyys" means significance and "analyysi" means analysis. In practice, merkittävyysanalyysi is equivalent to hypothesis testing in the broader field of statistics.

The central idea is to define a null hypothesis describing no effect or no difference and to calculate a p‑value, which measures the probability of obtaining results at least as extreme as those observed if the null hypothesis were true. If the p‑value falls below a predetermined threshold, usually 0.05, the null hypothesis is rejected and the difference is regarded as statistically significant.

Merkittävyysanalyysi is widely employed in biomedical research, agriculture, social sciences, and business analytics. For example, it is used to assess whether a new drug lowers blood pressure more than a placebo or whether a marketing campaign increases sales relative to previous periods. In genomics, a related technique, significance analysis of microarrays (SAM), identifies genes whose expression levels differ significantly between disease states.

Traditional merkittävyysanalyysi relies on parametric tests such as the t‑test and ANOVA, but non‑parametric alternatives like the Mann–Whitney U test or permutation tests are applied when data distributions violate assumptions. Recent advances incorporate multiple‑testing corrections, such as the false discovery rate, to address the increased risk of false positives when many hypotheses are tested simultaneously.