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Two-sample t-tests for a difference in means involve independent samples (unpaired samples) or paired samples. Paired t-tests are a form of blocking, and have greater power (probability of avoiding a type II error, also known as a false negative) than unpaired tests when the paired units are similar with respect to "noise factors" (see ...
Some tests perform univariate analysis on a single sample with a single variable. Others compare two or more paired or unpaired samples. Unpaired samples are also called independent samples. Paired samples are also called dependent. Finally, there are some statistical tests that perform analysis of relationship between multiple variables like ...
A paired difference test is designed for situations where there is dependence between pairs of measurements (in which case a test designed for comparing two independent samples would not be appropriate). That applies in a within-subjects study design, i.e., in a study where the same set of subjects undergo both of the conditions being compared.
The test is named after Frank Wilcoxon (1892–1965) who, in a single paper, proposed both it and the rank-sum test for two independent samples. [3] The test was popularized by Sidney Siegel (1956) in his influential textbook on non-parametric statistics. [4] Siegel used the symbol T for the test statistic, and consequently, the test is ...
In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...
If the paired observations are numeric quantities (such as the actual length of the hind leg and foreleg in the Zar example), and the differences between paired observations are random samples from a single normal distribution, then the paired t-test is appropriate. The paired t-test will generally have greater power to detect differences than ...
Equivalence T-Tests: Test the difference between two means with an interval-null hypothesis. JAGS: Implement Bayesian models with the JAGS program for Markov chain Monte Carlo. Learn Bayes: Learn Bayesian statistics with simple examples and supporting text. Learn Stats: Learn classical statistics with simple examples and supporting text.
Experimental methods are very popular in psychology, going back more than 100 years. Experimental psychology is a sub-discipline of psychology . Statistical methods applied for designing and analyzing experimental psychological data include the t-test, ANOVA, ANCOVA, MANOVA, MANCOVA, binomial test, chi-square, etc.