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This Hypothesis Testing topic will provide the participant with the knowledge and skills necessary to translate practical problems into statistical questions that are suitable for analytical investigation. Naturally, the ability to formulate statistical questions is essential to the valid integration and investigation of data that results from random sampling. Whenever a representative sample is used to draw an inference about the corresponding population, certain statistical hypotheses must be established and tested. Of course, such analytical questions are referred to as statistical hypotheses. Hence, this topic represents the first step into the world of sampling statistics, also called inferential statistics.
The participant will also learn how to establish and estimate the probabilistic risk of falsely accepting or rejecting null, alternate and directional hypotheses. In addition, the participant will learn how to determine the proper sample size for virtually any real-world application and understand how the sample size can influence decision confidence and risk. Furthermore, this topic will prepare a participant to undertake the study and application of such tools as Applied Diagnostic Methods (ADM), Statistical Process Control (SPC) and Design of Experiments (DOE). From this perspective, it is easy to see why Hypothesis Testing is at the heart of so many modern process improvement tools, methods and practices.
Reinforcement of major concepts, techniques, and application is realized through exercises, scenarios, and case studies. The following prerequisite topics are listed in sequential learning order: Basic Statistics. Total instructional time for this topic is 6 hours and 6 minutes.
- Statistical Inferences - Explain the concept of a statistical inference and its primary benefits
- Statistical Questions - Explain the nature and purpose of a statistical question
- Statistical Problems - Understand why practical problems must be translated into statistical problems
- Null Hypotheses - Define the nature and role of null hypotheses when making process improvements
- Alternate Hypotheses - Define the nature and role of alternate hypotheses when making process improvements
- Statistical Significance - Explain the concept of statistical significance versus practical significance
- Alpha Risk - Explain the concept of alpha risk in terms of the alternate hypothesis
- Beta Risk - Define the meaning of beta risk and how it relates to test sensitivity
- Criterion Differences - Explain the role of a criterion difference when testing hypotheses
- Decision Scenarios - Develop a scenario that exemplifies the use of hypothesis testing
- Sample Size - Define the statistical elements that must be considered when computing sample size