F Test for Two Variances Calculator
Compares two normal-population variances using their sample variance ratio. The form displays F=s1²/s2² beside f test for two variances, using a worked condition that can be recalculated with the labeled inputs.
Supply the analysis inputs
F test for two variances
The question behind f test for two variances
The f test for two variances page compares two normal-population variances using their sample variance ratio.
F test for two variances is limited to the statistical quantity named by the result panel. The f test for two variances calculation does not silently add a population, time horizon, causal direction, or decision threshold that is absent from the fields.
How the inputs shape f test for two variances
- Sample 1 variance: For f test for two variances, the worked value for sample 1 variance is 36 squared units. Treat the sample 1 variance entry (36 squared units) explicitly as a count, proportion, rate, estimate, or model parameter before comparing f test for two variances conditions. The form enforces minimum 1e-06.
- Sample 1 size: For f test for two variances, the worked value for sample 1 size is 20 observations. Treat the sample 1 size entry (20 observations) explicitly as a count, proportion, rate, estimate, or model parameter before comparing f test for two variances conditions. The form enforces minimum 2.
- Sample 2 variance: For f test for two variances, the worked value for sample 2 variance is 16 squared units. Treat the sample 2 variance entry (16 squared units) explicitly as a count, proportion, rate, estimate, or model parameter before comparing f test for two variances conditions. The form enforces minimum 1e-06.
- Sample 2 size: For f test for two variances, the worked value for sample 2 size is 18 observations. Treat the sample 2 size entry (18 observations) explicitly as a count, proportion, rate, estimate, or model parameter before comparing f test for two variances conditions. The form enforces minimum 2.
The entries used for f test for two variances must refer to one coherent analysis condition. Combining incompatible populations, periods, or measurement definitions can produce valid f test for two variances arithmetic for a nonexistent study.
How f test for two variances is calculated
For f test for two variances, match every symbol in the relationship to a labeled field before substituting numbers. F test for two variances is reported in the scale implied by the inputs and formula.
While checking f test for two variances, inspect every denominator in F=s1²/s2². For f test for two variances, a zero or near-zero denominator can make f test for two variances undefined or unstable.
Checking the displayed example
The default f test for two variances condition is Sample 1 variance = 36 squared units, Sample 1 size = 20 observations, Sample 2 variance = 16 squared units, Sample 2 size = 18 observations.
The example gives F=2.25 with df 19 and 17 and a two-sided p-value near 0.10.
The live calculator reports F statistic 2.25 · Numerator df 19 · Denominator df 17 · Two-sided p-value 0.09871303. Repeating one intermediate step from F=s1²/s2² provides a fixed f test for two variances reference check for later code changes.
What the f test for two variances arithmetic assumes
The classical F test is highly sensitive to nonnormal data and to the order used in the numerator.
For f test for two variances, a p-value measures compatibility with a stated null model; it is not the probability that the null hypothesis is true and it does not measure practical importance.
Reading f test for two variances in context
When interpreting f test for two variances, pair the test result with the effect direction, effect size, uncertainty, sampling design, and the rule used for one-sided or two-sided inference.
As a second check for f test for two variances, reversing the numerator and denominator answers a different question, so retain the direction printed in F=s1²/s2².
Testing how stable f test for two variances is
Change sample 1 variance while holding the remaining entries fixed, then state why the direction and size of the f test for two variances change are plausible from F=s1²/s2².
Repeat the f test for two variances exercise with sample 2 size. If a modest defensible change materially alters the interpretation, report both conditions rather than presenting that f test for two variances scenario as exact.
Rebuilding this f test for two variances calculation later
Report f test for two variances using F=s1²/s2², followed by the entered values, units, exclusions, and analysis date. Name the f test for two variances population or dataset boundary instead of leaving it implicit.
Keep the full calculator output with the record, including F statistic 2.25 · Numerator df 19 · Denominator df 17 · Two-sided p-value 0.09871303. A later f test for two variances review can then distinguish a changed input from a different convention or software implementation.
The surrounding workflow may also require one way anova, levene test, mcnemar test, and bartlett test.
Questions about f test for two variances
Why could another program report a different f test for two variances?
A different convention for rounding, tails, ties, interpolation, parameterization, or missing values can change f test for two variances. Compare the printed f test for two variances formula and its input definitions before treating either output as wrong.
What does f test for two variances represent on this page?
It is the quantity produced by F=s1²/s2² from the displayed sample 1 variance, sample 1 size, sample 2 variance, sample 2 size. This page compares two normal-population variances using their sample variance ratio.
What should be saved with f test for two variances?
Save the entered values and units for sample 1 variance, sample 1 size, sample 2 variance, sample 2 size, along with the analysis date, exclusions, software or formula version, and the relationship F=s1²/s2². That record is sufficient to rebuild this specific f test for two variances calculation.
Does f test for two variances establish a causal or population conclusion?
No. The displayed f test for two variances value is conditional on the entered data and named method. The f test for two variances design, measurement process, and assumptions determine what can be concluded beyond those values.
How should f test for two variances be rounded?
Keep the unrounded f test for two variances for subsequent arithmetic, then report only the precision supported by the source measurements and the decision context. Extra digits in f test for two variances do not correct sampling or model error.