Hypothesis Testing with Excel and the QI Macros

Hypothesis testing helps identify ways to reduce costs and improve quality. Hypothesis testing asks the question: Are two or more sets of data are the same or different, statistically:

What's cool about Hypothesis Testing in the QI Macros?
Unlike other statistical software, the QI Macros is the only SPC software that compares the p-values to the significance level and tells you when to "Accept or Reject the Null Hypothesis." The QI Macros results also tell you: "Means are Same or Different or Variances are Same or Different. "

Watch the Hypothesis Testing in Excel Video

In Six Sigma, hypothesis testing helps identify differences between machines, formulas, raw materials, etc. and are the differences statistically significant or not. Without such testing, teams can run around changing machine settings, formulas and so on causing more variation. These knee-jerk responses can amplify variation and cause more problems than doing nothing at all.

In manufacturing, you might want to compare two or more types of raw materials and determine if they produce the same quality. In other words, do the products have the same or different means and variances? If they are the same, which one is less expensive to produce? If they are different, which one best meets the customer's requirements?

There are Three Types of Hypothesis Tests

• Classical Method - comparing a test statistic to a critical value
• p value Method - the probability of a test statistic being contrary to the null hypothesis
• Confidence Interval Method - is the test statistic between or outside of the confidence interval

Setting Up a Hypothesis Test

First, you will need to define a null (H₀) and an alternate (H_a) hypothesis.

By default, the null hypothesis assumes that differences among observations are due to chance. In other words, it assumes that the means, averages or variation are statistically the same. The goal is to prove that they are not statistically the same at some level of confidence (usually 95%, 99%).

Tests can be either two sided or one sided depending on how the null hypothesis is stated.

Then, using data from the test:

1. Calculate the test statistic (t test, f test, z test, ANOVA, etc.).
   The test statistic is often converted to a p value (probability), but not always.
2. Compare the test statistic to:
   • a significance level (a) or confidence level (1-a)
   • a critical value (e.g., F_crit)
   to determine if you can accept or reject the null hypothesis.

Determining the Correct Test Statistic

Consider your data.
Is it variable (i.e., measured - 3.4 lbs) or attribute (i.e., counted - 3 defects)?
Are there one, two or more samples?

Are you comparing the means or variance?

Type I and II Errors

Hypothesis testing seeks to determine if the means or variances are the same or different at some level of confidence. Since we can never be totally confident, it is possible to encounter two types of errors:

• Type I error - Reject a null hypothesis that is true (Producer's Risk)
• Type II error - Not reject a null hypothesis that is false (Consumer's Risk)

Choose a confidence (or significance) level that will minimize the risk associated with these errors.

Watch the Hypothesis Testing in Excel Video

All of these hypothesis tests are included in the QI Macros Statistical Process Control software.

Download the FREE 30-day Evaluation copy of the QI Macros Excel SPC Software for Six Sigma