# Type I and type II errors/Self-check assessment

Use the following quiz to check your understanding of type I and type II errors. Note that as soon as you have indicated your response, the question is scored and feedback is provided. As feedback is provided for each option, you may find it useful to try all of the responses (both correct and incorrect) to read the feedback, as a way to better understand the concept.

Understanding type I and type II errors
• It has been shown many times that on a certain memory test, recognition is substantially better than recall. However, the probability value for the data from your sample was .12, so you were unable to reject the null hypothesis that recall and recognition produce the same results. What type of error did you make?
• Type I
• That's not quite right. A type I error can only occur if the significance test results in a p value small enough to reject the null hypothesis, because a type I error is when you reject the null hypothesis when in fact it is true.
• Type II
• That's correct. In this example, there is an actual difference in the population between recognition and recall, but you did not find a significant difference in your sample. Failing to reject a false null hypothesis is a type II error.
• In the population, there is no difference between men and women on a certain test. However, you found a difference in your sample. The probability value for the data was .03, so you rejected the null hypothesis. What type of error did you make?
• Type I
• That's correct. There is no difference in the population, but you found a difference in your sample. A Type I error occurs when a significance test results in the rejection of a true null hypothesis.
• Type II
• That's not quite right. A type II error can only occur if the significance test fails to reject the null hypothesis. A type II error is related to power. Sometimes it's helpful to note that a type II error would occur when a study fails to reject the null hypothesis when there was not enough power to find the TRUE difference.
• Beta, β, is the probability of which kind of error?
• Type I
• That's not quite right. Note that β is the second letter in the Greek alphabet, suggesting it is the probability of the second kind of error, type II.
• Type II
• That's correct. The probability of a type II error is called beta, β. The probability of correctly rejecting a false null hypothesis equals 1- β and is called power.
• As the alpha level gets lower, which error rate also gets lower?
• Type I
• That's correct. The type I error rate is affected by the alpha level; the lower the alpha level is, the lower the type I error rate gets. Alpha is the probability of a type I error given that the null hypothesis is true. When you specify an alpha level ahead of time, you are specifying the level of chance of a Type I error which you are willing to allow.
• Type II
• That's not quite right. Alpha is the probability of rejecting the null hypothesis when in fact it is true. A type II error, also termed a β error, occurs when in fact the null hypothesis is false.
• If the null hypothesis is in reality false, which kind of error is not possible?
• Type I
• That's correct. A Type I error occurs when a significance test results in the rejection of a TRUE null hypothesis.
• Type II
• That's not quite right. A type II error can only occur when the null hypothesis is false. Remember that a type II error is not really an error, but rather reflects an inconclusive result, a lack of power to detect the true alternate state of the population.

## Acknowledgements

The questions included in this quiz were adapted from