Cochran's C test

In statistics, Cochran's C test ^[1], 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 ^[2][3][4] and has been recommended by IUPAC ^[5] and ISO ^[6]. 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 F_max test ^[3], but Hartley's F_max 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.

Description

The C test detects one exceptionally large variance value at a time. The corresponding data series is then omitted from the full data set. According to ISO standard 5725 ^[6] the C test may be iterated until no further exceptionally large variance values are detected, but such practice may lead to excessive rejections if the underlying data series are not normally distributed. The C test evaluates the ratio:

$C_j = \frac{S_j^2}{\displaystyle \sum_{i=1}^N S_i^2}$

where:

C_j = Cochran's C statistic for data series j

S_j = standard deviation of data series j

N = number of data series that remain in the data set; N is decreased in steps of 1 upon each iteration of the C test

S_i = standard deviation of data series i (1 ≤ i ≤ N)

The C test tests the null hypothesis (H₀) against the alternative hypothesis (H_a):

H₀: All variances are equal.

H_a: At least one variance value is significantly larger than the other variance values.

Critical values

The variance value of data series j is considered an outlier at significance level α if C_j exceeds the upper limit critical value C_UL. C_UL depends on the desired significance level α, the number of considered data series N, and the number of data points (n) per data series. Selections of C_UL values have been tabulated at significance levels α = 0.01 ^[6][7][8], α = 0.025 ^[8] and α = 0.05 ^[6][7][8]. C_UL can also be calculated from ^[8][9]:

$C_\text {UL}(\alpha,n,N) = \left [ 1+ \frac{N-1}{F_\text {c}(\alpha/N,(n-1),(N-1).(n-1))} \right ]^{-1}$

Where:

C_UL = upper limit critical value for one-sided test on a balanced design

α = significance level

n = number of data points per data series

F_c = critical value of Fisher's F ratio; F_c can be obtained from tables ^[10] or using the FINV function in Excel ^[11]

Generalization

The C test can be generalized to include unbalanced designs, one-sided lower limit tests and two-sided tests at any significance level α, for any number of data series N, and for any number of individual data points n_j in data series j ^[8][9].

See also

• Bartlett's test
• Levene's test
• Brown–Forsythe test
• Hartley's test
• F-test of equality of variances

External links

• Critical C values ^[7]
• Generalized Variance Outlier Test ^[9]
• Critical F values ^[10]

References

1. ^ W.G. Cochran, The distribution of the largest of a set of estimated variances as a fraction of their total, Annals of Human Genetics (London) 11(1), 47–52 (January 1941).
2. ^ D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. de Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics: Part A, Elsevier, Amsterdam, The Netherlands, 1997 ISBN 0-444-89724-0.
3. ^ ^{a b} P. Konieczka, J. Namieśnik, Quality Assurance and Quality Control in the Analytical Chemical Laboratory – A Practical Approach, CRC Press, Boca Raton, Florida, 2009; ISBN 978-1-4200-8270-8.
4. ^ J.K. Taylor, Quality Assurance of Chemical Measurements, 4^th printing, Lewis Publishers, Chelsea, Michigan, 1988; ISBN 0-87371-097-5.
5. ^ W. Horwitz, Harmonized protocol for the design and interpretation of collaborative studies, Trends in Analytical Chemistry 7(4), 118–120 (April 1988).
6. ^ ^{a b c d} ISO Standard 5725–2:1994, “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”, International Organization for Standardization, Geneva, Switzerland, 1994; http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=11834
7. ^ ^{a b c} R. Moore, Mathematics Department, Macquarie University, Sydney, Australia, 1999: http://faculty.washington.edu/heagerty/Books/Biostatistics/TABLES/Cochran.
8. ^ ^{a b c d e} R.U.E. 't Lam, Scrutiny of variance results for outliers: Cochran's test optimized, Analytica Chimica Acta 659, 68–84 (2010); doi:10.1016/j.aca.2009.11.032.
9. ^ ^{a b c} R.U.E. 't Lam, Variance Outlier Test, blog: http://rtlam.blogspot.com/
10. ^ ^{a b} Table of critical values of the F-distribution:http://www.itl.nist.gov/div898/handbook/eda/section3/eda3673.htm
11. ^ Microsoft Excel 2003, Microsoft Corporation, Redmond, Washington, 1985-2003.