What are type I and type II errors? In the category of Research & Program Evaluation on the National Counselor Examination (NCE), the Counselor Preparation Comprehensive Examination (CPCE), or another counseling exam, you’ll want to know about Type 1 and Type 2 errors for the best chances at succeeding on your assessments.
Topics related to research and statistics can be intimidating and tricky to understand! In this post, we’re going over Type I and Type II errors to help you better understand these concepts!
To discuss Type I and Type II errors, we should first review the concept of the null hypothesis as this is an important part of these errors.
When we conduct an experiment, we will have at least two hypotheses. The experimental hypothesis is what you feel might happen in the experiment, or what you hypothesize the results might be. The null hypothesis is when there are no differences between the experimental group and the control group in the hypothesis. In other words, the null hypothesis is stating that the experiment did not produce the results that were expected.
This leads us to errors. No matter how great the statistical test is, chance can always affect the test and result in error. Type I and Type II errors are generally those that are discussed that can occur from statistical tests.
Also known as alpha errors, Type I errors mean that the null hypothesis is rejected, even though it is true. In other words, there is a false positive.
For Type II errors, also called beta errors, the null hypothesis is accepted even though it is false. To say that in a different way, the null hypnosis is not rejected even though it should be. This means there is a false negative.
The chances of a Type I (or alpha) error occurring is equal to the level of significance, which is the same as the probability that the null hypothesis will not be rejected.
With Type I and Type II errors, the chances of one occurring goes up as the other goes down. Rosenthal says to think of them as a see-saw. As the chances of a Type I error occurring goes up, the chances of a Type II error occurring will go down. On the other hand, as the chances of a Type I error occurring go down, the chances of a Type II error occurring go up. However, if an experiment has a larger sample size, this will lower the risk of both Type I and Type II errors occurring.
Example 1:
As an example to illustrate these errors, let’s imagine we have a test that is intended to show that a person is competent to become a therapist. If the person is actually competent but the test says they’re not, this is a Type I error. If the person is not competent to become a therapist but the test does not catch this and says they’re ready, this is a Type II error.
Example 2:
Here’s the next example. If there is no fire but the fire alarms go off, this is a Type I error. If there is a fire though and the fire alarms do not go off, this is a Type II error.
Example 3:
Let’s look at one more example. If we’re studying a new approach to therapy and the test says that this approach does not work when it actually does, this is a Type I error. If the test says the approach works when it actually does not work, this is a Type II error.
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