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In statistics, the Behrens–Fisher problem, named after Walter-Ulrich Behrens and Ronald Fisher, is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples.
The example discussed by Duncan in his 1955 paper is of a comparison of many means (i.e. 100), when one is interested only in two-mean and three-mean comparisons, and general p-mean comparisons (deciding whether there is some difference between p-means) are of no special interest (if p is 15 or more for example).
One problem is that, when g is not small, the confidence interval can blow up when using Fieller's theorem. Andy Grieve has provided a Bayesian solution where the CIs are still sensible, albeit wide. [2] Bootstrapping provides another alternative that does not require the assumption of normality. [3]
However, the studentized range distribution used to determine the level of significance of the differences considered in Tukey's test has vastly broader application: It is useful for researchers who have searched their collected data for remarkable differences between groups, but then cannot validly determine how significant their discovered ...
The original work on Multiple Comparisons problem was made by Tukey and Scheffé. Their method was a general one, which considered all kinds of pairwise comparisons. [7] Tukey's and Scheffé's methods allow any number of comparisons among a set of sample means.
In mathematics, the solution set of a system of equations or inequality is the set of all its solutions, that is the values that satisfy all equations and inequalities. [1] Also, the solution set or the truth set of a statement or a predicate is the set of all values that satisfy it. If there is no solution, the solution set is the empty set. [2]
The two-sample Hodges–Lehmann statistic is an estimate of a location-shift type difference between two populations. For two sets of data with m and n observations, the set of two-element sets made of them is their Cartesian product, which contains m × n pairs of points (one from each set); each such pair defines one difference of values.
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...