What statistical technique would be best used to determine if a PM program change actually reduced equipment failures?

To determine if a PM (Preventive Maintenance) program change actually reduced equipment failures, a statistical technique best suited for this purpose is a Before-and-After Comparison using a T-test or a similar statistical test for comparing means. This involves collecting data on equipment failure rates (e.g., number of failures per month) for a period *beforethe PM program change and then collecting similar data for a period *afterthe change. A T-test is then used to compare the means of the failure rates in the two periods to determine if the difference is statistically significant. Statistical significance means that the observed difference is unlikely to have occurred by chance alone. For example, if the average failure rate before the PM change was 5 failures per month and the average failure rate after the change was 2 failures per month, a T-test would determine if this reduction of 3 failures per month is statistically significant. Other factors to consider are ensuring the "before" and "after" periods are sufficiently long to account for natural variations and that no other significant changes occurred during the evaluation that could impact failure rates (e.g., equipment upgrades, changes in operating conditions).