Course Content:

Introduction to Hypotheses in Lean Six Sigma

Explanation of hypotheses and their importance in Lean Six Sigma.
The role of hypotheses in data-driven decision-making.
Key Concepts in Hypothesis Testing

Understanding key terms and concepts:
Null hypothesis (H0) and alternative hypothesis (H1)
Significance level (alpha)
Test statistic and p-value
How these concepts relate to hypothesis testing.

Formulating Hypotheses
Techniques for formulating clear and testable hypotheses.
Differentiating between null and alternative hypotheses.
Setting up hypotheses in Lean Six Sigma projects.

Types of Hypothesis Tests
Overview of common hypothesis tests used in Lean Six Sigma (e.g., t-tests, chi-square tests, ANOVA).
Selecting the appropriate test for the data and research question.

Hypothesis Testing Process
Step-by-step procedure for hypothesis testing in Lean Six Sigma:
Setting significance levels
Collecting data
Calculating test statistics and p-values
Making decisions based on the test results

Participants work on formulating hypotheses and conducting hypothesis tests using sample data.
Review and discussion of test results.

Interpreting Hypothesis Test Results
Techniques for interpreting test results and making data-driven decisions.
The significance of p-values and confidence intervals.