| Location | Cause and Solution |
| Normality Hypothesis Rejected | Switch to the nonparametric test from the same family, or address the cause (outlier, transformation). |
| Hypothesis of equal variances rejected | Choose the test that does not require it, or take it into account when interpreting the results. |
| Outliers Detected | Understand them before taking action. Remove them only if their cause has been identified and is unrelated to the process. |
| «There is no hypothesis to test for this test.» | This is normal for a nonparametric test. |
| The result is not statistically significant, even though the difference appears clear | Check the matching and sample size. If you want to conclude that the difference is negligible, switch to the «Show equivalence» objective—the link below the results card takes you there directly. |
| «Add a new sample to run the Chi-square test» | The test requires at least two samples. |
| «Not enough assembly/disassembly cycles to calculate the limits» | Repeat the assembly and disassembly cycles. |
| Significant ANOVA without knowing which groups differ | Work in pairs to draw a conclusion. |
| δ must be strictly positive | The field is empty or negative. No calculation will be performed until it is corrected. |
| »Zero variance" in equivalence | All values are identical: no test can be calculated; the conclusion is based solely on the observed difference. Interpret with caution. |
| δ far exceeds the observed dispersion | The tolerance is so wide that the test will conclude that the results are equivalent no matter what happens. Verify its business relevance. |
| The lens selector is gone | Equivalence is tested between two A maximum of samples, or one sample per target. Beyond that, the option is not available. |
Messages and Special Cases
Reading time