Central Limit Theorem Tutorial
The Central Limit Theorem (CLT) is critical to understanding inferential
statistics and hypothesis testing. This tutorial uses an applet
with exercises to demonstrate CLT concepts visually and interactively.
Goals of this tutorial: The goals of this exercise are (1) to illustrate interactively
the basic principles of the CLT, and (2) to demonstrate when it is
possible to assume that the sampling distribution of the mean is
reasonably normal. The assumption of normality of the sampling
distribution underlies
many inferential statistical applications and tests of statistical significance.
What do I need to know? To make best use of this tutorial, you should know how z
scores are related to probabilities on a normal
distribution. You should have an understanding of basic descriptive statistics such as the
mean
and standard
deviation. Familiarity with the sampling distribution of the mean will be helpful,
but not required. You may want to review the
Sampling Distribution of the
Mean Tutorial
before this you begin tutorial.
What do I need? A piece of paper will be useful for making
certain calculations, such as calculating zscores, and recording
responses to certain questions. A calculator may also be handy for
making these calculations. To convert zscores
from a standardized normal distribution to probability values, you may either use a table for
the standardized normal distribution (z) or the WISE pz converter.
Exercise instructions: You will be guided through a series of reviews (R),
activities (A), and questions (Q). For each question, simply click on your answer choice and
submit it (by either clicking the "Check answer" button or pressing "Enter" on the keyboard).
Then you will be given feedback. If you get the answer wrong, read the feedback
carefully to help you choose the correct response.
Begin the CLT tutorial.
hits since January 2006.
Questions, comments, difficulties? See our
technical support page or contact us: wise@cgu.edu.
