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In statistics, the Tukey–Duckworth test is a two-sample location test – a statistical test of whether one of two samples was significantly greater than the other. It was introduced by John Tukey, who aimed to answer a request by W. E. Duckworth for a test simple enough to be remembered and applied in the field without recourse to tables, let alone computers.
The one-sample location test compares the location parameter of one sample to a given constant. An example of a one-sample location test would be a comparison of the location parameter for the blood pressure distribution of a population to a given reference value.
Since the null hypothesis for Tukey's test states that all means being compared are from the same population (i.e. μ 1 = μ 2 = μ 3 = ... = μ k), the means should be normally distributed (according to the central limit theorem) with the same model standard deviation σ, estimated by the merged standard error, , for all the samples; its ...
This means that if we test the null hypothesis that the center of a Gaussian scale mixture distribution is 0, say, then t n G (x) (x ≥ 0) is the infimum of all monotone nondecreasing functions u(x) ≥ 1/2, x ≥ 0 such that if the critical values of the test are u −1 (1 − α), then the significance level is at most α ≥ 1/2 for all ...
John Wilder Tukey (/ ˈ t uː k i / [2]; June 16, 1915 – July 26, 2000) was an American mathematician and statistician, best known for the development of the fast Fourier Transform (FFT) algorithm and box plot. [3] The Tukey range test, the Tukey lambda distribution, the Tukey test of additivity, and the Teichmüller–Tukey lemma all bear
Tukey–Duckworth test: tests equality of two distributions by using ranks. Wald–Wolfowitz runs test: tests whether the elements of a sequence are mutually independent/random. Wilcoxon signed-rank test: tests whether matched pair samples are drawn from populations with different mean ranks.
Tukey's test is either: Tukey's range test , also called Tukey method, Tukey's honest significance test, Tukey's HSD (Honestly Significant Difference) test Tukey's test of additivity
Siegel–Tukey test, named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to data measured at least on an ordinal scale. It tests for differences in scale between two groups. The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.