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Peer Review

Type I and Type II Errors - Making Mistakes in the Justice System

Reviewed:

Sep 19, 2005 by Statistics
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Ratings

Overall Rating: 4.3 stars
Content Quality: 3.9 stars
Effectiveness: 3.8 stars
Ease of Use: 5.0 stars
Overview:

This item compares the concepts of hypothesis testing (including Type I and Type
II errors) to the American judicial system. There is also an applet which
allows students to examine the Type I and Type II errors based on the evidence
against the null hypothesis (the z-score) and the actual truth--sliders let
students explore how Type I and Type II errors work together.
Type of Material:

Text introduction with graphics and Java applet.
Recommended Uses:

This material could be used as a lecture example using the applet as part of a
demonstration. It could also be used in a laboratory or out of class for
students to explore the relationships between type I and type II errors and
power.
Technical Requirements:

Java enabled browser.
Identify Major Learning Goals:

Major learning goals are:
1) give students a good way to attach the jargon of hypothesis testing to the
more familar jargon of a trial;
and
2) allow students to explore relationships between Type I and Type II errors
together through the use of an applet.
Target Student Population:

The target student population is most likely students in introductory statistics
courses. However, this could also be used as a refresher for students in any
discipline who need to be reminded about the logic of hypothesis testing.
Prerequisite Knowledge or Skills:

Students need to know about the normal distribution and that z-scores represent
the number of standard deviations away from the mean. Students should also have
been introduced to hypothesis testing so that they have a brief background of
the material.

Content Quality

Rating:
Strengths:

The text illustration of this item is nicely written and fun to read. It uses
an easily understood analogy of a criminal trial. Additionally, the
applet is very helpful for understanding the relationship between Type I and
Type II errors. This is done by allowing the user to move the location of the
true distribution relative to the distribution under the null hypothesis.
Concerns:

1. The text description here sometimes discusses an error and the probability of
an error interchangably. These are distinct concepts, and the instructor
needs to help students understand this.
2. The authors incorrectly state that alpha is "equal to the p-value." Most
statisticians would agree that alpha is the significance level.
3. The authors don't make it explicit that alpha and beta are inversely related
only if the amount of information is assumed constant. In fact, if more
information is gathered, then it is possible to reduce both probabilities
because the relevant sampling distributions become less variable.
4. The false notion that researchers always want to evaluate the alternative
hypothesis is perpetuated.
5. In this applet only greater than alternatives are allowed. The instructor
should be sure to explain to students that other alternatives are available.

Potential Effectiveness as a Teaching Tool

Rating:
Strengths:

The applet is useful for visually and dynamically illustrating the connection
between alpha and beta when the amount of information is held constant. The
applet could be helpful if a series of guided questions were written to
accompany this material, or if the instructor used it in class where he/she
could ask the questions and explain.

Concerns:

The text explanation is quite similar to a textbook explanation. The included
applet will need specific questions for the student to help them explore the
relationship. The instructor may also want to supplement this applet with
connections to real world examples other than the trial analogy.

Ease of Use for Both Students and Faculty

Rating:
Strengths:

Very easy to use. The applet layout is uncluttered and intuitive (no pun with
the URL intended).
Concerns:

None.
Other Issues and Comments:

The trial analogy is good for definining and differentiating between Type I and
Type II errors; however, the analogy becomes awkward and labored as it continues
to be used to define and illustrate the probabilities of these errors. It is
probably better to switch to an actual statistical hypothesis test (a familiar
one from a homework assignment) to explain and illustrate alpha and beta.
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