pväärtust

pväärtust refers to the p-value, a statistical measure used in hypothesis testing to quantify the compatibility of observed data with a specified null hypothesis. Formally, the p-value is the probability of obtaining results at least as extreme as those observed, assuming that the null hypothesis is true and the statistical model and assumptions (such as sampling distribution) hold.

The p-value is computed from a test statistic derived from the sample data (for example, t, z, chi-square) and the known or assumed distribution of that statistic under the null hypothesis. Small p-values indicate that the observed data are unlikely under the null hypothesis, which may lead researchers to reject the null in favor of an alternative hypothesis; conventionally, thresholds like 0.05 or 0.01 are used, though these cutoffs are arbitrary.

Interpretation requires care: a p-value is not the probability that the null hypothesis is true, nor does it measure the size or importance of an effect. It is sensitive to sample size and can be affected by violations of model assumptions, selective reporting, multiple testing, and p-hacking. Because of these limitations, many researchers recommend supplementing or replacing p-values with effect size estimates, confidence intervals, Bayesian measures (such as Bayes factors or posterior probabilities), and transparent study design practices like pre-registration and correction for multiple comparisons.

Historically central to inferential statistics, p-values remain widely used but are increasingly treated as one part of a broader evidentiary framework rather than a sole determinant of scientific conclusions.