InsightFab
Engineering Knowledge Base
355 articles. Plain-English explanations of Minitab/JMP statistical tools.
4 articles
Box-Cox Transformation: The Right Way to Make Non-Normal Data Normal
Many statistical analyses (t-test, ANOVA, Cpk) assume data follows a normal distribution. Box-Cox transformation is the most systematic method, automatically finding the optimal transformation parameter to meet the normality assumption for data.
Cpk for Non-Normal Processes: What to Do When Data Doesn't Follow a Normal Distribution?
The process capability index Cpk assumes data follows a normal distribution. However, data such as stamping burrs, surface roughness, and leak rates are inherently non-normal. This article explains three calculation methods for non-normal process capability and the logic for their selection.
Kruskal-Wallis Test: When ANOVA's Assumptions Are Not Met
ANOVA is the standard tool for comparing means across three or more groups. However, when data does not meet normality assumptions, contains outliers, or is ordinal, the Kruskal-Wallis test is the appropriate alternative.
Normality Test: Does Your Data Qualify for Those Formulas?
You measured 30 parts, calculated Cpk, and confidently submitted the report, only to be asked by the QA manager if a normality test was performed. This is crucial because Cpk, t-test, and ANOVA all rely on the fundamental assumption that the data follows a normal distribution.