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Statistical

F.TEST Formula

Performs an F-test and returns the two-tailed p-value, testing whether two datasets have significantly different variances. This is useful as a preliminary check before running a t-test to decide whether to assume equal or unequal variances. It is also used in quality control to compare process variability.

Syntax

F.TEST(array1, array2)
ParameterDescription
array1 Parameter of the F.TEST function.
array2 Parameter of the F.TEST function.
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Examples

Compare process variability

Formula
=F.TEST(A2:A31, B2:B31)
Returns the two-tailed p-value comparing the variance of 30 measurements from process A versus process B. If p < 0.05, the processes have significantly different variability.

Pre-t-test variance check

Formula
=F.TEST(C2:C51, D2:D51)
Returns the p-value for variance equality. If p > 0.05, you can use an equal-variance t-test (type 2); otherwise, use Welch's t-test (type 3).

Comparing lab instruments

Formula
=F.TEST(E2:E21, F2:F21)
Tests whether two lab instruments produce measurements with different levels of precision (variance). A significant result suggests one is more variable than the other.

Common Errors

#DIV/0!

Occurs if either array has a variance of zero (all values identical), since the F-statistic involves dividing by variance.

#N/A

Returned if either array has fewer than 2 data points, since variance requires at least 2 observations.

Tips

Two-tailed by default

F.TEST returns a two-tailed p-value, testing for any difference in variance (larger or smaller). Divide by 2 if you specifically want a one-tailed test.

Sensitive to non-normality

The F-test for equal variances is notoriously sensitive to non-normal data. If your data is not roughly normal, consider Levene's test instead (not built into spreadsheets but can be constructed).

Not the same as ANOVA F-test

F.TEST compares variances of two groups. It is not an ANOVA. For comparing means across groups, calculate the F-statistic manually and use F.DIST.RT.

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