Cochran's Q Test

A Cochran's Q Test is a non-parametric statistical test of whether k treatments have identical outcomes.

Context:
- It tests the null hypothesis of whether k treatments are equally effective against the alternative hypothesis of whether the effectiveness among the treatment is different.
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Counter-Example(s)
- Cochran's C Test.
See: Nonparametric Statistics, Bartlett's Test, Levene's Test, Brown–Forsythe Test, Hartley's Test, Time Series, F distribution, Statistical Test, Regression Analysis, Outlier, Standard Deviation.

References

(Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Cochran's_Q_test Retrieved 2016-08-21
- In statistics, in the analysis of two-way randomized block designs where the response variable can take only two possible outcomes (coded as 0 and 1), Cochran's Q test is a non-parametric statistical test to verify whether k treatments have identical effects. It is named for William Gemmell Cochran. Cochran's Q test should not be confused with Cochran's C test, which is a variance outlier test. Put in less technical terms, requires that there only be a binary response (success/failure or 1/0) and that there be 2 or more matched groups (groups of the same size). The test assesses whether the proportion of successes is the same between groups. Often used to assess if different observers of the same phenomenon have consistent results amongst themselves (interobserver variability).