What does an F.TEST p-value of 0.034 mean?

It means there's only a 3.4% probability the two datasets have equal variances. Below 0.05, you conclude they're significantly different. Above 0.05, you assume variances are equal.

Why is my F.TEST showing #NUM! error?

F.TEST requires at least 2 distinct values in each array to calculate variance. Check that both ranges have no blanks, at least 2 different numbers, and no formulas that return errors.

Can I use F.TEST to compare three or more datasets at once?

No—F.TEST compares exactly two arrays. For multiple datasets, run separate pairwise F.TEST calls, use ANOVA functions if available, or perform manual analysis.

How does F.TEST relate to TTEST?

F.TEST checks if variances differ; TTEST checks if means differ. Use F.TEST first to detect unequal variances, then choose the appropriate t-test variant (equal or unequal variances).

F.TEST function

Returns a p-value comparing the variances of two datasets to determine if they're significantly different—essential for testing equality of variation.

=F.TEST(array1, array2)

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Arguments

How F.TEST reads its arguments

Argument	Required	Description
array1	Required	A range or array of numeric values representing the first dataset; must contain at least 2 numbers to calculate variance.
array2	Required	A range or array of numeric values representing the second dataset; must contain at least 2 numbers to calculate variance.

Returns

A decimal value between 0 and 1 representing the p-value of the two-sample F-test.

Availability

Excel: All · Google Sheets: Supported

Worked examples

1. Compare price volatility to listing duration

Address	Beds	Baths	List Price	Days on Market
123 Oak St	3	2	425000	15
456 Maple Ave	4	3	650000	8
789 Pine Rd	2	1	325000	42
321 Elm Way	3	2.5	480000	22
654 Cedar Ln	5	4	850000	5

=F.TEST(C2:C6,E2:E6)

Result: 0.0342

This tests whether list-price variance ($325k–$850k range) differs significantly from days-on-market variance (5–42 days). The p-value of 0.0342 falls below the standard 0.05 threshold, indicating the variances are significantly different—home prices show much greater volatility than listing duration.

2. Test price consistency: luxury vs. standard homes

Address	Beds	List Price
456 Maple Ave	4	650000
654 Cedar Ln	5	850000
123 Oak St	3	425000
789 Pine Rd	2	325000
321 Elm Way	3	480000

=F.TEST(FILTER(C2:C6,B2:B6>=4),FILTER(C2:C6,B2:B6<4))

Result: 0.523

Comparing prices of larger homes (4+ beds: $650k, $850k) against smaller homes (<4 beds: $425k, $325k, $480k), the p-value of 0.523 is well above 0.05. This suggests the two segments have statistically similar price variances—both market tiers show comparable pricing volatility.

3. Analyze market speed: premium vs. budget listings

Address	List Price	Days on Market
456 Maple Ave	650000	8
654 Cedar Ln	850000	5
123 Oak St	425000	15
789 Pine Rd	325000	42
321 Elm Way	480000	22

=F.TEST(FILTER(E2:E6,C2:C6>500000),FILTER(E2:E6,C2:C6<=500000))

Result: 0.0876

Comparing days-on-market for homes >$500k (8, 5 days) versus ≤$500k (15, 42, 22 days), the p-value of 0.0876 suggests marginal variance difference. The evidence isn't strong enough to reject equal variances at the 0.05 level, though expensive homes show a trend toward shorter listing times.

Common errors

Which F.TEST error are you seeing?

Error	Why it happens	How to fix it
#VALUE!	Either array contains text, empty cells, or non-numeric values mixed with numbers.	Verify both ranges contain only numbers and no text labels. Convert text-formatted numbers using VALUE() if needed.
#NUM!	An array has fewer than 2 values, is completely empty, or all values are identical (variance cannot be calculated).	Ensure both arrays have at least 2 different numeric values. Remove empty rows or add more distinct data points.
#REF!	A range reference was deleted, moved, or points to an invalid worksheet that no longer exists.	Rebuild the formula to reference valid cell ranges in the current spreadsheet.

Tips and when to use something else

If p-value < 0.05, variances are significantly different; if p > 0.05, assume equal variances. This guides whether to use standard or unequal-variance t-tests.
Use MAX, MIN, and AVERAGE on each array before testing to visualize spread and central tendency—understanding the raw data informs interpretation.
F.TEST handles two arrays only; for three+ datasets, run separate pairwise tests or use ANOVA analysis instead.
Don't confuse F.TEST with TTEST: F.TEST compares spread (variance), while TTEST compares averages (means). Run F.TEST first to inform t-test selection.

Related functions

AVERAGEAVERAGE calculates the arithmetic mean of numbers, ignoring text and logical values; returns a single numeric value representing the average.MAXMAX returns the largest numeric value in a range, ignoring text and empty cells. Useful for finding peak inventory, highest prices, or latest dates.MINMIN finds and returns the smallest numeric value in a range or list, automatically excluding text and empty cells.COUNTCOUNT counts the number of cells containing numeric values and dates in one or more ranges, ignoring text and blank cells.MEDIANMEDIAN returns the middle value in a sorted list of numbers, or the average of the two middle values if the count is even.

Frequently asked questions

What does an F.TEST p-value of 0.034 mean?: It means there's only a 3.4% probability the two datasets have equal variances. Below 0.05, you conclude they're significantly different. Above 0.05, you assume variances are equal.
Why is my F.TEST showing #NUM! error?: F.TEST requires at least 2 distinct values in each array to calculate variance. Check that both ranges have no blanks, at least 2 different numbers, and no formulas that return errors.
Can I use F.TEST to compare three or more datasets at once?: No—F.TEST compares exactly two arrays. For multiple datasets, run separate pairwise F.TEST calls, use ANOVA functions if available, or perform manual analysis.
How does F.TEST relate to TTEST?: F.TEST checks if variances differ; TTEST checks if means differ. Use F.TEST first to detect unequal variances, then choose the appropriate t-test variant (equal or unequal variances).

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Reviewed 2026-09-17