Hypothesis Testing Calculator
t-tests, z-tests, chi-square and ANOVA — automatic test statistics and p-values with AI interpretation.
All tools
1-sample t
2-sample t
Paired t
1-sample z
2-sample z
1 variance
2 variances (F)
Chi-square GOF
Chi-square independence
One-way ANOVA
One-sample t-test
Tests whether the sample mean equals a hypothesized value (e.g. is the average dimension equal to the spec center).
At least 2 data points
Two-sample t-test
Tests whether the means of two independent samples are equal (e.g. output before vs. after an improvement).
Paired t-test
Tests whether the mean of paired differences is zero (e.g. before/after measurement of the same units, training scores).
Both groups must have the same number of data points
Chi-square goodness-of-fit test
Tests whether the observed distribution matches an expected distribution (e.g. defect type mix vs. historical proportions).
Actual counts for each category
Expected proportion per category, summing to 1; leave empty for a uniform distribution
Chi-square test of independence
Tests whether two categorical variables are independent (e.g. is shift related to defect rate).
One group per row, one category per column
One-way ANOVA
Tests whether multiple group means are all equal (e.g. do 3 machines produce significantly different dimensions).
At least 2 groups, each with at least 2 data points
One-sample z-test (σ known)
When the population std dev σ is known, tests whether the sample mean equals a hypothesized value.
Two-sample z-test (σ known)
When population std devs σ₁, σ₂ are known, tests whether two group means are equal (common for large batches).
Single variance test (χ²)
Tests whether the variance / std dev of a process equals a target value (e.g. does the process variation meet the requirement).
At least 2 data points
Two variance (F) test
Tests whether two variances are equal (equal-variance assumption for t-tests, before/after variation comparison).
Test result
