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Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary.
Pearson's chi-squared test (χ 2 )—one of a variety of different chi-squared tests —is a statistical procedure used with category data to decide if experimental results are statistically significant, or else can reasonably be explained by mere chance. Like most statistical tests, it compares observed frequencies (under some kind of test ...
The null hypothesis of this chi-squared test is homoscedasticity, and the alternative hypothesis would indicate heteroscedasticity. Since the Breusch–Pagan test is sensitive to departures from normality or small sample sizes, the Koenker–Bassett or 'generalized Breusch–Pagan' test is commonly used instead.
Many common test statistics are tests for nested models and can be phrased as log-likelihood ratios or approximations thereof: e.g. the Z -test, the F -test, the G -test, and Pearson's chi-squared test; for an illustration with the one-sample t -test, see below.
In probability theory, the multinomial distribution is a generalization of the binomial distribution. For example, it models the probability of counts for each side of a k -sided dice rolled n times. For n independent trials each of which leads to a success for exactly one of k categories, with each category having a given fixed success ...
F. -test of equality of variances. In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance. Notionally, any F -test can be regarded as a comparison of two variances, but the specific case being discussed in this article is that of two populations, where the test ...
In statistics, Fisher's method, [1] [2] also known as Fisher's combined probability test, is a technique for data fusion or "meta-analysis" (analysis of analyses). It was developed by and named for Ronald Fisher. In its basic form, it is used to combine the results from several independence tests bearing upon the same overall hypothesis ( H0 ).
Kruskal–Wallis test. The Kruskal–Wallis test by ranks, Kruskal–Wallis test [1] (named after William Kruskal and W. Allen Wallis ), or one-way ANOVA on ranks [1] is a non-parametric method for testing whether samples originate from the same distribution. [2] [3] [4] It is used for comparing two or more independent samples of equal or ...