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.

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.