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Six Sigma Tools & Templates Hypothesis Testing

Hypothesis Testing

A Solution Template to Help in Hypothesis Testing

A difficult topic for those learning statistics is hypothesis testing. Solving several problems will convince new Six Sigma practitioners of the importance of this tool. And a solution template can ease the difficulties of the learning process.

Hypothesis Testing: Fear No More

New Belts may be anxious about using statistical tools, but the process doesn’t need to be daunting. By learning how to test for normality, select the right test and interpret its results, Belts can be prepared rather than scared.

Making Sense of Mann-Whitney Test for Median Comparison

When conducting the 2-sample t-test to compare the average of two groups, the data must be sampled from normally distributed populations. If that assumption does not hold, the nonparametric Mann-Whitney test is a better for drawing conclusions.

Making Sense of the Two-Proportions Test

Use a two-proportions hypothesis test to determine whether a Six Sigma project actually improved the process. The test compares the percentages of two groups and only works when the raw data behind the percentages is available.

Making Sense of the Two-Sample T-Test

The two-sample t-test is one of the most commonly used hypothesis tests in Six Sigma work. It is applied to compare whether the average difference between two groups is really significant or if it is due instead to random chance.

Nonparametric: Distribution-Free, Not Assumption-Free

Nonparametric analysis methods are essential tools in the Black Belt's analytic toolbox. When appropriately applied, nonparametric methods are often more powerful than parametric methods if the assumptions for the parametric model cannot be met.

Rejected! The Ugly Truth About Hypothesis Testing

Most people use p 0.05 as the line where they reject the null in hypothesis testing. Yet p 0.05 means there is still a risk of making a false assertion five percent of the time. Correctly rejecting a null hypothesis is about more than just p-value.

Reporting Format for Hypothesis Testing

By following a consistent reporting format, a Six Sigma team and its customers can better understand and explain hypothesis test results and conclusions.

Understanding the Uses for Mood’s Median Test

The Mood's median test is used to test the equality of medians from two or more populations and holds no assumptions about specific distribution. Therefore, it provides a nonparametric alternative to the one-way ANOVA, which requires normality.

Using Efficient Process Tolerance and Mean Shift Tests

The sequential probability ratio test, or SPRT, can be used as an efficient tool for process tolerance and mean shift determinations. It also provides for simplifying insights into the nature of random mean shifts.

Using the 1-Sample Sign Test for Paired Data

Although the paired t-test will work for normally distributed sets of paired data, a nonparametric alternative must be used for non-normal data: the 1-sample sign test. This test makes it possible to compare observed and hypothesized medians.

Using the Power of the Test for Good Hypothesis Testing

Rejecting a null hypothesis when it is false is what every good hypothesis test should do. The “power of the test” is the measure of how good a test is. It is the probability that the test will reject Ho when in fact it is false.

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