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In [[statistics]], '''Bartlett's test''' (see [[George W. Snedecor|Snedecor]] and [[William Gemmell Cochran|Cochran]], 1989) is used to test if ''k'' samples are from populations with equal [[variance]]s. Equal variances across samples is called [[homoscedasticity]] or homogeneity of variances. Some statistical tests, for example the [[analysis of variance]], assume that variances are equal across groups or samples. The Bartlett test can be used to verify that assumption. | |||
Bartlett's test is sensitive to departures from normality. That is, if the samples come from non-normal distributions, then Bartlett's test may simply be testing for non-normality. [[Levene's test]] and the [[Brown–Forsythe test]] are alternatives to the Bartlett test that are less sensitive to departures from normality.<ref>''NIST/SEMATECH e-Handbook of Statistical Methods''. Available online, URL: http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm. Retrieved December 31, 2013.</ref> | |||
The test is named after [[M. S. Bartlett|Maurice Stevenson Bartlett]]. | |||
==Specification== | |||
Bartlett's test is used to test the null hypothesis, ''H''<sub>0</sub> that all ''k'' population variances are equal against the alternative that at least two are different. | |||
If there are ''k'' samples with size <math>n_i</math> and [[sample variance]]s <math>S_i^2</math> then Bartlett's test statistic is | |||
:<math>X^2 = \frac{(N-k)\ln(S_p^2) - \sum_{i=1}^k(n_i - 1)\ln(S_i^2)}{1 + \frac{1}{3(k-1)}\left(\sum_{i=1}^k(\frac{1}{n_i-1}) - \frac{1}{N-k}\right)} | |||
</math> | |||
where <math>N = \sum_{i=1}^k n_i</math> and <math>S_p^2 = \frac{1}{N-k} \sum_i (n_i-1)S_i^2</math> is the pooled estimate for the variance. | |||
The test statistic has approximately a <math>\chi^2_{k-1}</math> distribution. Thus the null hypothesis is rejected if <math>X^2 > \chi^2_{k-1,\alpha}</math> (where <math>\chi^2_{k-1,\alpha}</math> is the upper tail critical value for the <math>\chi^2_{k-1}</math> distribution). | |||
Bartlett's test is a modification of the corresponding [[likelihood ratio test]] designed to make the approximation to the <math>\chi^2_{k-1}</math> distribution better (Bartlett, 1937). | |||
==References== | |||
{{Reflist}} | |||
*Bartlett, M. S. (1937). "Properties of sufficiency and statistical tests". ''Proceedings of the Royal Statistical Society'', Series A 160, 268–282 {{jstor|96803}} | |||
*Snedecor, George W. and Cochran, William G. (1989), ''Statistical Methods'', Eighth Edition, Iowa State University Press. ISBN 978-0-8138-1561-9 | |||
==External links== | |||
* [http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm NIST page on Bartlett's test] | |||
{{statistics}} | |||
[[Category:Analysis of variance]] | |||
[[Category:Statistical tests]] | |||
Revision as of 21:34, 22 December 2013
In statistics, Bartlett's test (see Snedecor and Cochran, 1989) is used to test if k samples are from populations with equal variances. Equal variances across samples is called homoscedasticity or homogeneity of variances. Some statistical tests, for example the analysis of variance, assume that variances are equal across groups or samples. The Bartlett test can be used to verify that assumption.
Bartlett's test is sensitive to departures from normality. That is, if the samples come from non-normal distributions, then Bartlett's test may simply be testing for non-normality. Levene's test and the Brown–Forsythe test are alternatives to the Bartlett test that are less sensitive to departures from normality.[1]
The test is named after Maurice Stevenson Bartlett.
Specification
Bartlett's test is used to test the null hypothesis, H0 that all k population variances are equal against the alternative that at least two are different.
If there are k samples with size and sample variances then Bartlett's test statistic is
where and is the pooled estimate for the variance.
The test statistic has approximately a distribution. Thus the null hypothesis is rejected if (where is the upper tail critical value for the distribution).
Bartlett's test is a modification of the corresponding likelihood ratio test designed to make the approximation to the distribution better (Bartlett, 1937).
References
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- Bartlett, M. S. (1937). "Properties of sufficiency and statistical tests". Proceedings of the Royal Statistical Society, Series A 160, 268–282 Template:Jstor
- Snedecor, George W. and Cochran, William G. (1989), Statistical Methods, Eighth Edition, Iowa State University Press. ISBN 978-0-8138-1561-9
External links
- ↑ NIST/SEMATECH e-Handbook of Statistical Methods. Available online, URL: http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm. Retrieved December 31, 2013.