KnowraLikelihood-ratio testLinked fromLinked fromThe 14 pages that link to Likelihood-ratio test, each with the reason it gives.All 14Broader topic 5Related 6Compared with 3P-valueRelated: It is a test procedure that often produces a p-value, not an alternative interpretation of one.Likelihood ratioBroader topic: This test uses a likelihood ratio to assess whether a more flexible model improves fit.Statistical hypothesis testingRelated: It is a specific testing method whose statistic measures relative fit of the two models.Bayes factorCompared with: It uses best-fitting parameter values, whereas a Bayes factor averages over parameter uncertainty.Model selectionRelated: It tests whether a larger nested model improves fit enough to justify its extra parameters.Sufficient statisticRelated: Sufficient summaries can sometimes preserve the likelihood ratios needed for inference.Neyman–Pearson lemmaBroader topic: The lemma identifies this test as most powerful at a fixed significance level.Null hypothesis significance testingCompared with: It provides a model-comparison decision rule that need not be framed as a p-value threshold.Chi-squared distributionRelated: Under regularity conditions, twice the log-likelihood ratio is asymptotically chi-squared.Frequentist inferenceRelated: It provides a common test statistic whose calibration is often frequentist.F-distributionCompared with: For nested models, it can address restrictions like an F-test, often with an asymptotic chi-square reference.Statistical hypothesis testBroader topic: It assesses whether a more complex model improves fit enough over a nested null model.Parametric statisticsBroader topic: It tests parameter restrictions by comparing a broader model with a constrained one.Detection theoryBroader topic: It implements detection by comparing how well each hypothesis explains the same observation.