tilgátupróf

Tilgátupróf, or hypothesis testing, is a statistical method used to assess whether a claimed statement about a population parameter should be supported by data. It involves evaluating evidence against a default position called the null hypothesis and an alternative hypothesis that represents the claim of interest.

In practice, the null hypothesis (H0) expresses no effect or no difference, while the alternative hypothesis (H1 or Ha) states the effect or difference that the researcher wants to detect. The process typically includes choosing a significance level (alpha), commonly 0.05, selecting an appropriate test, calculating a test statistic, and deciding whether the observed data are unlikely under H0. A p-value estimates the probability of obtaining results as extreme or more extreme than those observed if H0 is true. If the p-value is at most alpha, H0 is rejected in favor of Ha; otherwise, there is not enough evidence to reject H0.

Common tests include z-tests and t-tests for means, chi-square tests for categorical data, and ANOVA for comparing multiple groups. Nonparametric alternatives exist when assumptions about the data are violated. Assumptions such as random sampling, independence, and distributional requirements influence which test is appropriate. The framework also considers Type I error (rejecting a true H0) and Type II error (failing to reject a false H0), with statistical power measuring the ability to detect true effects.

Results are often reported with an effect size and a confidence interval to convey practical significance. Hypothesis testing complements, rather than replaces, other approaches and can be complemented by Bayesian methods that quantify evidence differently. Limitations include sensitivity to sample size and assumptions, potential for p-hacking, and the need to interpret results in context.