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In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .
The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on a sample of data, or to compare the locations of two populations using two matched samples. [1]
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
A two-sample location test of the null hypothesis such that the means of two populations are equal. All such tests are usually called Student's t -tests , though strictly speaking that name should only be used if the variances of the two populations are also assumed to be equal; the form of the test used when this assumption is dropped 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 ...
The typical steps involved in performing a frequentist hypothesis test in practice are: Define a hypothesis (claim which is testable using data). Select a relevant statistical test with associated test statistic T. Derive the distribution of the test statistic under the null hypothesis from the assumptions.
The one-sample test statistic, , for Kuiper's test is defined as follows. Let F be the continuous cumulative distribution function which is to be the null hypothesis . Denote by F n the empirical distribution function for n independent and identically distributed (i.i.d.) observations X i , which is defined as
The logrank test, or log-rank test, is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right skewed and censored (technically, the censoring must be non-informative).