Hypothesis Testing


Overview/Description
Target Audience
Expected Duration
Lesson Objectives
Course Number



Overview/Description
What if you proposed a solution to a problem at your company based only on a strong hunch and casual review of data? What if your company bought into your solution and invested millions of dollars executing it? If the solution didn't work as you expected or the end results were insignificant, chances are you'd be looking for a new job. When millions of dollars and, perhaps, your company's future success are on the line, your hypotheses must be based on more than hunches or strong feelings. Six Sigma® uses complex statistical tools to analyze data from multiple angles to be sure proposed solutions or hypotheses are truly the right ones for your company. Hypothesis testing refers to the process of using statistical analysis to determine whether the observed differences between two or more samples are due to random chance or to true differences in the samples. This course, Hypothesis Testing, explores the statistical techniques used to test hypotheses in Six Sigma projects. It covers how data can be viewed in a variety of ways and how sample size impacts data. Additionally, this course explores confidence levels and the statistical techniques used to confirm that a hypothesis is indeed valid and the end results statistically significant. Six Sigma is a registered Trademark of Motorola Corporation, and all right, title and interest in Six Sigma belongs to Motorola.

Target Audience
Candidates for Black Belt certification; managers/executives overseeing personnel involved in the implementation of Six Sigma in their organization; consultants involved in implementing a Six Sigma proposal; and organizations implementing a Six Sigma project

Expected Duration (hours)
2.5

Lesson Objectives

Fundamental Concepts of Hypothesis Testing

  • identify the benefits of using statistical tools for hypothesis testing.
  • sequence the steps for hypothesis testing.
  • match the steps for hypothesis testing with examples.
  • match the factors that determine statistical significance with their defining characteristics.
  • make recommendations about how to manipulate the factors for determining statistical significance based on their defining characteristics.
  • calculate the standard error in a given scenario.
  • match the factors for calculating sample size to their definitions.
  • determine the appropriate value for each of the factors for calculating sample size when presented with a case study.
  • Point and Interval Estimation

  • identify the advantages associated with avoiding statistical bias.
  • identify the area or areas in which a point estimator displays desirable characteristics.
  • match the approaches for measuring differences between two groups with their characteristics.
  • translate statistical statements based on information about the factors for measuring statistical accuracy.
  • Course Number:
    OPER0252