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Purpose
The F-Test Two-Sample for Variances analysis
tool performs a two-sample F-test to compare two population variances.
This criterion is parametric. To
check it use Normality Tests. See also
Why the "Normal distribution" is
important.
For example, you can use an F-test to
determine whether the time scores in a swimming meet have a difference in
variance for samples from two teams.
Preparations
To run this procedure, select a range, and then run the Statistics→Basic
Statistics and Tables→F-Test for Variances...
command
Results
Count - analyzed sample size.
Mean -
analyzed sample mean. See
Elementary Concepts.
Standard Error of the Mean, p-level - See
Elementary Concepts.
F - test statistic.
P(F<=f)(Probability, corresponding to Fisher criterion) - represents the probability of error involved in accepting our research
hypothesis about the existence of a difference. Technically speaking, this is
the probability of error associated with rejecting the hypothesis of no
difference between the two categories of observations (corresponding to the
groups) in the population when, in fact, the hypothesis is true.
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