LocationEquivalenceTest
LocationEquivalenceTest[{data_{1},data_{2},…}]
tests whether the means or medians of the data_{i} are equal.
LocationEquivalenceTest[{data_{1},…},"property"]
returns the value of "property".
Details and Options
 LocationEquivalenceTest performs a hypothesis test on the data_{i} with null hypothesis that the true location parameters of the populations are equal, , and alternative hypothesis that at least one is different.
 By default, a probability value or value is returned.
 A small value suggests that it is unlikely that is true.
 The data_{i} must be univariate {x_{1},x_{2},…}.
 LocationEquivalenceTest[{data_{1},…}] will choose the most powerful test that applies to the data.
 LocationEquivalenceTest[{data_{1},…},All] will choose all tests that apply to the data.
 LocationEquivalenceTest[{data_{1},…},"test"] reports the value according to "test".
 Meanbased tests assume that the data_{i} are normally distributed. The medianbased Kruskal–Wallis test assumes that data_{i} are symmetric about a common median. The complete block and Friedman rank tests assume that the data is in randomized complete blocks. All of the tests require the data_{i} to have equal variances.
 The following tests can be used:

"CompleteBlockF" normality, blocked mean test for complete block design "FriedmanRank" blocked median test for complete block design "KruskalWallis" symmetry median test for two or more samples "KSampleT" normality mean test for two or more samples  The complete block test effectively performs oneway analysis of variance for randomized complete block design.
 The Friedman rank test ranks observations across rows and sums the ranks along columns in the data to arrive at the test statistic. The statistic is corrected for ties.
 The Kruskal–Wallis test effectively performs a oneway analysis of variance on the ranks of the data. The test statistic is corrected for ties.
 The sample test is equivalent to a oneway analysis of variance of the data.
 LocationEquivalenceTest[{data_{1},…},"HypothesisTestData"] returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
 LocationEquivalenceTest[{data_{1},…},"property"] can be used to directly give the value of "property".
 Properties related to the reporting of test results include:

"AllTests" list of all applicable tests "AutomaticTest" test chosen if Automatic is used "DegreesOfFreedom" the degrees of freedom used in a test "PValue" list of values "PValueTable" formatted table of values "ShortTestConclusion" a short description of the conclusion of a test "TestConclusion" a description of the conclusion of a test "TestData" list of pairs of test statistics and values "TestDataTable" formatted table of values and test statistics "TestStatistic" list of test statistics "TestStatisticTable" formatted table of test statistics  The following options can be given:

Method Automatic the method to use for computing values SignificanceLevel 0.05 cutoff for diagnostics and reporting VerifyTestAssumptions Automatic what assumptions to verify  For tests of location, a cutoff is chosen such that is rejected only if . The value of used for the "TestConclusion" and "ShortTestConclusion" properties is controlled by the SignificanceLevel option. This value is also used in diagnostic tests of assumptions, including tests for normality, equal variance, and symmetry. By default, is set to 0.05.
 Named settings for VerifyTestAssumptions in LocationEquivalenceTest include:

"Normality" verify that all data is normally distributed "EqualVariance" verify that the data_{i} have equal variances "Symmetry" verify symmetry about a common median
Examples
open allclose allBasic Examples (3)
Test whether the means or medians from two or more populations are all equivalent:
Create a HypothesisTestData object for repeated property extraction:
The complete block test can be used to test for mean differences with complete block design:
There is a significant difference among the means at the 0.05 level:
Use the Friedman rank test to test for differences in medians with complete block design:
It appears that at least one median differs significantly from the others:
Scope (9)
Testing (5)
Perform a particular test for equal locations:
Any number of tests can be performed simultaneously:
Perform all tests appropriate to the data simultaneously:
Use the property "AllTests" to identify which tests were used:
Create a HypothesisTestData object for repeated property extraction:
The properties available for extraction:
Extract some properties from a HypothesisTestData object:
The value and test statistic from a sample test:
Reporting (4)
Tabulate the results from a selection of tests:
A full table of all appropriate test results:
A table of selected test results:
Retrieve the entries from a test table for customized reporting:
The values are above 0.05, so there is not enough evidence to reject at that level:
Tabulate values for a test or group of tests:
A table of values from all appropriate tests:
A table of values from a subset of tests:
Report the test statistic from a test or group of tests:
Options (8)
Method (2)
Compute the Kruskal–Wallis test for a group of datasets:
The rescaled test statistic follows an FRatioDistribution:
Use the asymptotic chisquare approximation:
Use the asymptotic chisquare distribution for the Friedman rank test:
SignificanceLevel (3)
VerifyTestAssumptions (3)
Applications (4)
Test whether a group of populations shares a common location:
The first group of datasets was drawn from populations with very different locations:
Populations represented by the second group all have similar locations:
Morphological measures of two crab varieties were taken for each of the two sexes. Determine whether the measures differ across the various groups:
The rear width is the only measure that differs by gender when variety is ignored:
All measures are significantly different when gender and variety are considered simultaneously:
A pilot study was conducted for 75 patients with type II diabetes who had failed to achieve target weight loss with a particular medication. The patients were randomly assigned to three groups: a control group continuing the original medication, and two treatment groups that received 50 and 100 mg of a new medication, respectively. Weight loss in pounds over a 12week period was recorded:
There is a significant difference in the means of the groups:
Using a Bonferroni correction in a test of each pairwise difference shows that both treatment levels perform better than the control, but that they are not significantly different from one another:
A group of six food critics rated four restaurants for quality on a 100point scale. Determine whether there is a significant difference in the quality of the restaurants according to critics:
Bar charts of the median score by critic:
Bar charts of the median score for each restaurant:
Accounting for the blocked structure, a significant difference in quality can be detected:
Properties & Relations (12)
The value returned by a sample test is equivalent to that of TTest for two samples:
The Kruskal–Wallis test is a sample extension of the twosample Mann–Whitney test:
The Mann–Whitney value is corrected for continuity and ties:
Under the sample test statistic follows an FRatioDistribution[g1,ng], where g is the number of datasets and n is the total number of observations:
Under the complete block and Friedman rank test statistics with t treatments and g blocks follows an FRatioDistribution[t1,(g1)(t1)]:
The Friedman statistic can be transformed to follow a ChiSquareDistribution[g1]:
Compute a value using ChiSquareDistribution:
This transformation is done automatically with Method set to "Asymptotic":
Under the Kruskal–Wallis test statistic asymptotically follows a ChiSquareDistribution[g1] where g is the number of datasets:
By default, the test statistic is rescaled to follow an FRatioDistribution[g1,ng]:
Conceptually, a comparison is made between the pooled and average individual variances:
Larger pooled variances indicate different means:
The ratio of pooled to individual variances:
LocationEquivalenceTest effectively detects how far this ratio is from 1:
The and test statistics are used in LocationEquivalenceTest:
The Kruskal–Wallis statistic is rankbased:
For sample and Kruskal–Wallis tests, the statistic can be computed using LinearModelFit:
The Kruskal–Wallis test is identical but uses ranks:
Use LocationTest for two datasets:
LocationTest can also test more complicated hypotheses:
The location equivalence test ignores the time stamps when the input is a TimeSeries:
The location equivalence test recognizes the path structure of a TemporalData:
Possible Issues (3)
All of the tests require that the data has equal variances:
The sample test and complete block test require that the data is normally distributed:
The Kruskal–Wallis test or Friedman rank test should be used if the data is not normally distributed:
The Friedman rank and complete block tests require equal sample sizes: