"Per-family error rate (PFER) control" with AI and ML in automated testing means:

1. _Family-wise error rate (FWER) control_: AI and ML control the FWER, the probability of at least one Type I error in a family of tests.

2. _Holm-Bonferroni method_: AI and ML apply the Holm-Bonferroni method to adjust the alpha level for each test based on the number of tests in the family.

3. _Šidák correction_: AI and ML apply the Šidák correction to adjust the alpha level for each test based on the number of tests and the desired FWER.

4. _Dunnett's method_: AI and ML use Dunnett's method to control the FWER for multiple comparisons with a control group.

5. _Hochberg's GT2 method_: AI and ML apply Hochberg's GT2 method to control the FWER for multiple tests with unequal sample sizes.

6. _AI-optimized PFER algorithms_: AI and ML use machine learning algorithms to optimize PFER control, adapting to changing data distributions and test conditions.

7. _Dynamic PFER adjustment_: AI and ML dynamically adjust the PFER based on the test results, tightening or relaxing the error rate as needed.

8. _PFER-based test selection_: AI and ML select the most informative tests based on PFER control, optimizing test suites for efficiency and accuracy.

By controlling the Per-Family Error Rate (PFER), AI and ML in automated testing can:

- Reduce the number of false positives

- Improve the accuracy of test results

- Increase trust in automated testing

- Optimize test suites for efficiency and effectiveness