Module 11 Practice Questions (3)

Front

What does an F-Value in ANOVA indicate?

Back

The F-Value in ANOVA indicates the ratio of variance between the group means to the variance within the groups. A higher F-Value suggests a greater degree of difference between group means.

Front

What does a P-Value signify in hypothesis testing?

Back

A P-Value indicates the probability of observing the test results under the null hypothesis. A low P-Value (typically < 0.05) suggests that the null hypothesis can be rejected.

Front

What is the null hypothesis (H0)?

Back

The null hypothesis (H0) is a statement that there is no effect or no difference, and it serves as the default assumption in hypothesis testing.

Front

What does it mean to reject the null hypothesis?

Back

Rejecting the null hypothesis means that there is sufficient evidence to support the alternative hypothesis, indicating a statistically significant effect or difference.

Front

What is a matched pairs hypothesis test?

Back

A matched pairs hypothesis test is used when comparing two related groups, where each subject in one group is paired with a subject in the other group based on a specific characteristic.

Front

What is the significance level (alpha) in hypothesis testing?

Back

The significance level (alpha) is the threshold for determining whether to reject the null hypothesis, commonly set at 0.05.

Front

What does it mean if a study has a T-Value of 0.25?

Back

A T-Value of 0.25 suggests that the difference between the sample means is small relative to the variability in the data, indicating weak evidence against the null hypothesis.

Front

What is the purpose of conducting an ANOVA?

Back

The purpose of conducting an ANOVA is to determine whether there are statistically significant differences between the means of three or more independent groups.

Front

What does a confidence interval represent?

Back

A confidence interval represents a range of values that is likely to contain the population parameter with a specified level of confidence (e.g., 95%).

Front

What is the difference between Type I and Type II errors?

Back

A Type I error occurs when the null hypothesis is incorrectly rejected (false positive), while a Type II error occurs when the null hypothesis is not rejected when it is false (false negative).