Fred Schenkelberg — Thought Leader

Here’s an overview of the non-parametric test to evaluate if a set of samples have the same variance. If the variances are equal they have homogeneity of variances.

Some statistical tests assume equal variances across samples, such as analysis of variance and many types of hypothesis tests. It is also assumed for statistical process control purposes to determine stability (often done with range (r chart) or standard deviation (s charts). [Read more…]

A contingency table, as in the chi-squared test of independence, reveals if two sets of data or groups are independent or not. It does not reveal the strength of the dependence. The contingency coefficient is a non-parametric measure of the association for cross-classification data. [Read more…]

The chi-square ( $- \chi^2 -$) test provides a means to determine independence between two or more variables. In this case, it works for count data.

Contingency table or row and column (r x c) analysis are other common names for this analysis. It is useful when comparing results from different treatments or processes. [Read more…]

Here’s an overview of a distribution-free approach commonly called the Kaplan-Meier (K-M) Product Limit Reliability Estimator.

There are no assumptions about underlying distributions. And, K-M works with datasets with or without censored data. We do need to know when failures or losses (items removed from the evaluation or test other than as a failure. Censored items). [Read more…]

This is a non-parametric test to compare ranked data from three or more groups or treatments. The basic idea is to compare the mean value of the rank values and test if the samples could are from the same distribution or if at least one is not.

The null hypothesis is the data from each group would receive about the same mean rank score. We are comparing rank values, not the actual values. [Read more…]