Which statistical test is used to assess precision (variance)?

• A. T test
• B. F test
• C. Chi-square test
• D. ANOVA

Answer: (B)

Precision is about repeatability, so it centers on how dispersed the measurements are; this dispersion is captured by the variance. To judge whether the observed spread matches what you’d expect, you compare variances, and that’s what the F-test does. It looks at the ratio of two variances, and under the assumption that the variances are equal, this ratio follows an F distribution with the appropriate degrees of freedom. If the observed ratio is significantly larger (or smaller) than what the F distribution predicts, you conclude that the precision differs between the groups or from the target.

This is why the F-test is the natural choice for assessing precision. The t-test focuses on differences in means, not variability. ANOVA also centers on mean differences across multiple groups, using a statistic that reflects between-group vs. within-group variance, rather than directly testing a single variance. The chi-square test can be used to test a variance against a hypothesized value in some contexts, but for comparing precision across samples, the standard approach is the F-test.