A hypothesis is an assumption or assertion about a population parameter or a theory about the relationship between one variable and another, or between multiple variables. Because a hypothesis is initially unproven, use a hypothesis test during data analysis activities to determine the probability that a given hypothesis is true, determine the probability that the results of the hypothesis occurred by chance, determine if a real difference exists between two populations using relatively small samples, and quantify risks associated with the hypothesis test.
In this easy-to-follow eLearning course, equipped with engaging voice-over narration and compelling content-supporting visuals, animations, and hands-on interactions, gain a fundamental understanding of hypothesis testing, a type of test that uses statistics to determine the probability that a given hypothesis is true. Learn about common hypothesis testing concepts and terms like null hypothesis, alternative hypothesis, Type I error, Type II error, p-value, and power.
- CEU Hours: 0
- Length: 0.25 Hours
- ASQ RU: 0.03
- Audience: New to Quality
- Provider: ASQ
- Define and distinguish between hypothesis testing terms like null hypothesis, alternative hypothesis, Type I error, Type II error, p-value, and power
Who Should Attend:Anyone new to quality who wants to learn about hypothesis testing.
- Learning Objectives
- What is a Hypothesis Test?
- Null Hypothesis vs. Alternative Hypothesis
- Type I Errors
- Significance Level
- Confidence Level
- Type II Errors
- Statistical Power
FormatThis is a self-paced course that can be started at any time.
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Internet-based, self-paced training modules, which may involve combinations of text, visuals, audio, interactive simulations and quizzes (see specific courses for features and tools). These web-based courses require a computer and Internet access.
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