5.4.5 - Median Test - Teradata Warehouse Miner

Teradata Warehouse Miner User Guide - Volume 3Analytic Functions

Product
Teradata Warehouse Miner
Release Number
5.4.5
Published
February 2018
Language
English (United States)
Last Update
2018-05-04
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The Median test is a special case of the chi-square test with fixed marginal totals. It tests whether several samples came from populations with the same median. The null hypothesis is that all samples have the same median.

The median test is applied for data in similar cases as for the ANOVA for independent samples, except when the following occurs:
  • the data are either importantly non-normally distributed
  • the measurement scale of the dependent variable is ordinal (not interval or ratio)
  • or the data sample is too small.
The Median test is a less powerful non-parametric test than alternative rank tests due to the fact the dependent variable is dichotomized at the median. Because this technique tends to discard most of the information inherent in the data, it is less often used. Frequencies are evaluated by a simple 2 x 2 contingency table, so it becomes simply a 2 x 2 chi square test of independence with 1 DF.

Given k independent samples of numeric values, a Median test is produced for each set of unique values of the group-by variables (GBVs), if any, testing whether all the populations have the same median. Output for each set of unique values of the GBVs is a p-value, which when compared to the user’s threshold, determines whether the null hypothesis should be rejected for the unique set of values of the GBVs. For more than 2 samples, this is sometimes called the Brown-Mood test.