Cochran's C Test
A Cochran's C Test is a statistical test for the null hypothesis of whether all variances are equal against the alternative hypothesis of whether at least one variance value is significantly larger than the other variance values.
- Context:
- It is a test for detecting upper limit variance outliers.
- …
- Counter-Example(s)
- See: Bartlett's Test, Levene's Test, Brown–Forsythe Test, Hartley's Test, Time Series, F distribution, Statistical Test, Regression Analysis, Outlier, Standard Deviation.
References
2016
- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Cochran's_C_test Retrieved 2016-08-21
- In statistics, Cochran's C test, named after William G. Cochran, is a one-sided upper limit variance outlier test. The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable. The C test is discussed in many text books and has been recommended by IUPAC and ISO. Cochran's C test should not be confused with Cochran's Q test, which applies to the analysis of two-way randomized block designs.
The C test assumes a balanced design, i.e. the considered full data set should consist of individual data series that all have equal size. The C test further assumes that each individual data series is normally distributed. Although primarily an outlier test, the C test is also in use as a simple alternative for regular homoscedasticity tests such as Bartlett's test, Levene's test and the Brown–Forsythe test to check a statistical data set for homogeneity of variances. An even simpler way to check homoscedasticity is provided by Hartley's Fmax test, but Hartley's Fmax test has the disadvantage that it only accounts for the minimum and the maximum of the variance range, while the C test accounts for all variances within the range.
- In statistics, Cochran's C test, named after William G. Cochran, is a one-sided upper limit variance outlier test. The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable. The C test is discussed in many text books and has been recommended by IUPAC and ISO. Cochran's C test should not be confused with Cochran's Q test, which applies to the analysis of two-way randomized block designs.