The different samples are all
assumed to come from populations with the same variance,
allowing for a
pooled estimate of the variance.
For a multiple comparisons
test of the sample means to be meaningful,
the populations are viewed as fixed,
so that the populations in the experiment include all those of interest.
A one-way analysis of variance (ANOVA) tests whether any of the population
means differ from each other. A multiple comparisons test may be
used to answer the question of which population means differ from
which other means, a question the ANOVA itself will not answer.
Guidance:
Ways to detect before performing the
one-way ANOVA whether your data violate any assumptions.
Ways to examine one-way
ANOVA results to detect assumption violations.
Possible alternatives if your data or
one-way ANOVA results indicate assumption violations.
To properly analyze and interpret results of
one-way analysis of variance (ANOVA), you should be familiar with the following terms
and concepts:
Failure to understand and properly apply
one-way analysis of variance
(ANOVA) may result in drawing erroneous conclusions from your data.
If you are not familiar with these terms and concepts, you are advised to
consult with a statistician.
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.
Lindman, Harold R. 1992. Analysis of Variance in
Experimental Design. New York: Springer-Verlag.
Miller, Rupert G. Jr. 1996. Beyond ANOVA, Basics of Applied
Statistics. 2nd. ed.
London: Chapman & Hall.
Neter, J., Wasserman, W., and Kutner, M.H. 1990. Applied
Linear Statistical Models. 3rd ed. Homewood, IL: Irwin.
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