For the usual two-sample t test, the two different samples are
assumed to come from populations with the same variance,
allowing for a
pooled estimate of the variance.
However, if the two sample variances are clearly different, a variant
test, the
Welch-Satterthwaite t test,
is used to test whether the means are different.
Guidance:
Ways to detect before performing the
t test whether your data violate any
assumptions.
Ways to examine t test results to detect
assumption violations.
Possible alternatives if your data or t test
results indicate assumption violations.
To properly analyze and interpret
results of the two-sample unpaired t test,
you should be familiar with the following terms and
concepts:
Failure to understand and properly apply two-sample unpaired t tests
may result in drawing erroneous conclusions from your data.
If you are not familiar with these terms and concepts, you may wish to
consult with a statistician.
You may also 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.
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