Variance Tests--Testing equality of variances

• Chi-square test for variance (test whether population variance is equal to hypothesized value)
• F test (test whether two population variances are equal)
• Levene's test (test whether multiple population variances are equal)
• Bartlett's test (test whether multiple population variances are equal)
• Ansari-Bradley test (test nonparametrically whether two population dispersions (scales) are equal)

To properly analyze and interpret results of variance tests, you should be familiar with the following terms and concepts:

• importance of homogeneity of variance
• Gaussian (normal) distribution assumption
• violation of assumptions

If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply variance tests may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:

• Brownlee, K. A. 1965. Statistical Theory and Methodology in Science and Engineering. New York: John Wiley & Sons.
• Daniel, Wayne W. 1995. Biostatistics. 6th ed. New York: John Wiley & Sons.
• Miller, Rupert G. Jr. 1996. Beyond ANOVA, Basics of Applied Statistics. 2nd. ed. London: Chapman & Hall.
• Rosner, Bernard. 1995. Fundamentals of Biostatistics. 4th ed. Belmont, California: Duxbury Press.
• Sokal, Robert R. and Rohlf, F. James. 1995. Biometry. 3rd. ed. New York: W. H. Freeman and Co.
• Zar, Jerrold H. 1996. Biostatistical Analysis. 3rd ed. Upper Saddle River, NJ: Prentice-Hall.

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