Knowra Statistical significance Statistical significance Statistical significance means that observed data would be sufficiently unlikely under a specified null hypothesis, according to a chosen significance threshold. It does not by itself measure effect size, practical importance, or the probability that the hypothesis is true.
Null hypothesis : A statistical hypothesis specifying a reference condition, often no effect or no difference. Significance evaluates how surprising the data are if this reference condition holds.
Effect size : A quantitative measure of the magnitude of a difference, association, or other phenomenon. A small effect can be significant with enough data, while a large estimate can remain uncertain.
Bayesian hypothesis testing : A method for comparing hypotheses using prior probabilities and the likelihood of observed data. It can quantify evidence for competing hypotheses rather than apply a frequentist significance cutoff.
Fisher's exact test : A test of association in a contingency table that calculates probabilities under a fixed-margin null model. It is a named test whose p-value can determine whether categorical data are significant.
Ronald Fisher : A British statistician who developed influential methods for experimental design, estimation, and hypothesis testing. Fisher popularized the p-value and treated significance levels as aids to scientific judgment.
P-value : The probability, under a specified statistical model, of results at least as extreme as those observed. It quantifies the data’s extremeness under the null, not the probability the null is true.
Confidence interval : An interval-valued estimate produced by a method that achieves stated long-run coverage under its assumptions. Intervals show estimate precision that a significant-or-not label conceals.
Bayes factor : A ratio comparing how well two hypotheses predict observed data. Unlike a p-value, it directly compares the data’s support under two specified models.
Student's t-test : A family of tests for comparing means using a t-distribution under specified assumptions. Its test statistic and p-value are often used to assess significance between group means.
Jerzy Neyman : A Polish mathematician and statistician who helped develop frequentist statistical theory. Neyman formalized decision rules and long-run error rates that shaped modern testing.
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