Sadly, browsers no longer support the interactive Java applet that is featured in this tutorial.
Introduction to Hypothesis Testing
Overview: Statistical hypothesis testing is a method of making decisions about a population based on sample data. We can compute how likely it is to find specific sample data if the sample was drawn randomly from the hypothesized population. For example, we can determine if graduates of a training program on average obtain higher test scores than individuals who did not take this training program.
What do I need to know? To make best use of this exercise, you should know how the sampling distribution of the mean is related to sample size and the population variance. It would be helpful to have completed the WISE tutorials on the Sampling Distribution and Central Limit Theorem. A quick review of these topics and the normal distribution can be found at the bottom of this page.
What do I need? You will need a calculator to answer some questions. If you will need to submit your responses to your instructor, download the Tutorial Worksheet to use as you go through the tutorial.
Instructions: You will be asked questions and you will be given feedback regarding your answers. We provide detailed explanations, but you should try to answer the questions on your own before consulting our solutions. You will learn much more by doing the exercises yourself than if you merely read them and the answers.
At the end of the tutorial, you will be able to test your knowledge with our online quiz on hypothesis testing or gain further practice on a set of questions similar to those in the tutorial.
Optional review material:
- Review Sampling Distribution of the Mean
- Review Central Limit Theorem
- Review z-scores and the Normal Distribution
Suggested format for citing this tutorial:
Berger, D. E. & Saw, A. T. (2008). WISE Hypothesis Testing Tutorial. Retrieved [date] from http://wise.cgu.edu.
We would like to thank the following individuals for their work on an older version of a Hypothesis Testing tutorial which this version is based on: Chris Aberson, Michael Healy, Victoria Romero, and Diana Kyle.
11,544 total views, 5 views today