Hypothesis Testing and Statistical Errors Worksheet

What is a p-value?

• The probability the null hypothesis is true

• The probability of obtaining results at least as extreme as the observed result, assuming the null is true

• The percentage of the sample that rejected the null

• The probability of a Type I error

Which of the following represents a Type I error?

• Failing to reject a false null hypothesis

• Rejecting a true null hypothesis

• Accepting a false alternative hypothesis

• Rejecting a false null hypothesis

What is the consequence of increasing the alpha level from 0.01 to 0.05?

• Type II error rate increases

• Type I error rate decreases

• Type I error rate increases

• Power decreases

What is the obtained value in hypothesis testing?

• The alpha level used to compare the p-value

• The cutoff value for rejecting the null hypothesis

• The observed test statistic from your data (e.g., t, z, or F value)

When is a result considered statistically significant at an alpha level of 0.05?

• When the p-value is greater than 0.05

• When the p-value is less than or equal to 0.05

• When the null hypothesis is proven false

• When the confidence interval includes zero

What is a Type II error?

• Rejecting a true null hypothesis

• Failing to reject a false null hypothesis

• Finding a statistically significant effect that is not real

• Misinterpreting the p-value

Which of the following best describes the file drawer effect?

• Journals that prefer null results over significant ones

• A tendency to publish only statistically significant findings, while null results remain unpublished

• When too many studies are filed due to Type I errors

• When only Type II errors are reported

Which of the following is an example of a null hypothesis?

• Group A will score higher than Group B on the test

• There will be a difference in reaction times between two conditions
  

• There is no difference in blood pressure between the control and treatment groups

• Increased study time improves exam scores

What is a critical value in hypothesis testing?

• The smallest value the p-value can be

• The threshold at which we reject the null hypothesis

• The average value of the population

• The observed test statistic

Which of the following best explains why statistical significance does not imply practical significance?

• Statistical significance only applies to large populations

• A small effect can be statistically significant if the sample size is large

• Practical significance depends on the sample size only

• Statistical significance and effect size are the same