Hypothesis Testing for Root Cause Analysis

Prioritize potential causes using a Cause & Effect Matrix linked to CTQs.

Use graphical tools (e.g., Box Plots, Multi-Vari Charts) to visually explore process data for shifts and trends before formal testing.

Select the appropriate hypothesis test based on data type and the objective of the comparison.

Perform and interpret t-tests (1-sample, 2-sample, paired) to compare process means.

Conduct ANOVA to compare means across multiple groups (e.g., machines, shifts, suppliers).

Apply chi-square tests to analyze attribute data, such as defect counts by category.