"Statistical process control" with AI and ML in automated testing means:

1. _Monitoring process variability_: AI and ML track process data to detect deviations from expected ranges.

2. _Control charts_: AI and ML use charts like Shewhart, EWMA, and CUSUM to visualize and detect anomalies.

3. _Statistical models_: AI and ML apply models like regression, ARIMA, and exponential smoothing to forecast and detect anomalies.

4. _Hypothesis testing_: AI and ML perform tests like t-tests, ANOVA, and chi-squared to identify significant deviations.

5. _Confidence intervals_: AI and ML calculate intervals to determine acceptable ranges for process data.

6. _Process capability analysis_: AI and ML evaluate process performance using metrics like Cp, Cpk, and Pp.

7. _Root cause analysis_: AI and ML identify underlying causes of anomalies using statistical methods.

8. _Continuous improvement_: AI and ML refine process control limits and models based on new data.

By applying statistical process control with AI and ML, automated testing can:

- Detect subtle anomalies and trends

- Improve process understanding and optimization

- Reduce false alarms and unnecessary interventions

- Enhance overall process reliability and quality