Cochran C test for outlying variances
Cochran's C test helps determining if one sample has a different variance from the variances of a set of samples. It is available in Excel using XLSTAT.
What is Cochran's C test
The Cochran’s C test (Cochran 1941) is one of the tests developed to identify and study the homogeneity of a series of variances (Bartlett's test, Brown-Forsythe, Levene or Hartley in particular). Cochran's test was developed to answer a specific question: Are the variances homogeneous or is the highest variance different from the others. XLSTAT also offers two alternatives and uses the results of 't Lam (2010) for an extension of the balanced case to unbalanced cases.
In statistics, an outlier is a value recorded for a given variable, that seems unusual and suspiciously lower or greater than the other observed values. One can distinguish two types of outliers:
- An outlier can simply be related to a reading error (on an measuring instrument), a keyboarding error, or a special event that disrupted the observed phenomenon to the point of making it incomparable to others. In such cases, you must either correct the outlier, if possible, or otherwise remove the observation to avoid that it disturbs the analyses that are planed (descriptive analysis, modeling, predicting).
- An outlier can also be due to an atypical event, but nevertheless known or interesting to study. For example, if we study the presence of certain bacteria in river water, you can have samples without bacteria, and other with aggregates with many bacteria. These data are of course important to keep. The models used should reflect that potential dispersion.
When there are outliers in the data, depending on the stage of the study, we must identify them, possibly with the aid of tests, flag them in the reports (in tables or on graphical representations), delete or use methods able to treat them as such.
To identify outliers, there are different approaches. For example, in classical linear regression, we can use the value of Cook’s d values, or submit the standardized residuals to a Grubbs test to see if one or two values are abnormal. The classical Grubbs test can help identifying one outlier, while the double Grubbs test allows identifying two. It is not recommended to use these methods repeatedly on the same sample. However, it may be appropriate if you really suspect that there are more than two outliers.
If the sample can be divided into sub-samples, we can look for changes from a sub-sample to another. The test Cochran’s C test and the Mandel’s h and k statistics are part of the methods suitable for such studies.
Results with XLSTAT
The results correspond to the Cochran’s C test are displayed. An interpretation of the test is provided if a single iteration of the test was requested, or if no observation was identified as being an outlier.
In case several iterations were required, also display a table showing, for each observation, the iteration in which it was removed from the sample.
The z-scores are displayed if they have been requested.
Barnett V. and Lewis T. (1980). Outliers in Statistical Data. John Wiley and Sons, Chichester, New York, Brisbane, Toronto.
Cochran W.G. (1941). The distribution of the largest of a set of estimated variances as a fraction of their total. Ann. Eugen. 11, 47-52.
Hawkins D.M. (1980). Identification of Outliers. Chapman and Hall, London.
International Organization for Standardization (1994). ISO 5725-2: Accuracy (trueness and precision) of measurement methods and results—Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method, Geneva.