Tag: nonparametric

When comparing the average of two or more groups with the help of hypothesis tests, the assumption is that the data is a sample from a normally distributed population. That is why hypothesis tests such as the t-test, paired t-test, and analysis of variance (ANOVA) are also called parametric tests. Nonparametric tests do […]

In the Analyze phase of a DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma project, potential root causes of variations and defects are identified and validated. Various data analysis tools are used for exploratory and confirmatory studies. Descriptive and graphical techniques help with understanding the nature of data and visualizing potential relationships. Statistical analysis techniques, […]

The paired t-test is used to check whether the average differences between two samples are significant or due only to random chance. In contrast with the “normal” t-test, the samples from the two groups are paired, which means that there is a dependency between them. The following example illustrates the difference between the regular t-test […]

When conducting the 2-sample t-test to compare the average of two groups, the data in both groups must be sampled from a normally distributed population. If that assumption does not hold, the nonparametric Mann-Whitney test is a better safeguard against drawing wrong conclusions. The Mann-Whitney test compares the medians from two populations and works when […]

Nonparametric or distribution-free methods have several advantages or benefits. They may be used on all types of data including nominal, ordinal, interval and ratio scaled. They make fewer and less stringent assumptions than their parametric counterparts. Depending on the particular procedure, nonparametric methods may be almost as powerful as the corresponding parametric procedure when the […]