"Hypothesis testing" with AI and ML in automated testing means:

1. _Formulating hypotheses_: AI and ML define null and alternative hypotheses based on process expectations.

2. _Data sampling_: AI and ML select representative samples from process data.

3. _Test selection_: AI and ML choose appropriate tests (t-tests, ANOVA, chi-squared, etc.) based on data type and hypothesis.

4. _Test execution_: AI and ML perform statistical tests, calculating p-values and confidence intervals.

5. _Result interpretation_: AI and ML determine whether results indicate significant deviations (rejecting the null hypothesis).

6. _Type I error control_: AI and ML adjust testing parameters to minimize false positives (Type I errors).

7. _Multiple testing correction_: AI and ML apply corrections (Bonferroni, Holm-Bonferroni, etc.) for multiple tests.

8. _Continuous refinement_: AI and ML update hypotheses and testing parameters based on new data and process changes.

By performing hypothesis testing with AI and ML, automated testing can:

- Objectively evaluate process performance

- Identify statistically significant deviations

- Support data-driven decision-making

- Improve process understanding and optimization

- Reduce errors and improve quality