1. What is the relationship between the power of a statistical test and decision errors?

• Powerful tests minimize the risk of decision errors.
• Powerful tests are more inclined to type II than type I errors.
• Powerful tests compensate for decision errors with stronger effect sizes.
• Powerful tests minimize type II errors.

2. If workers' productivity is analyzed in terms of whether or not they took a training course and which shift they work, how many Fs will be calculated?

• 2
• 3
• 4
• 5

3. Which is the symbol used for the test statistic in ANOVA?

• M
• z
• t
• F

4. The one-sample t-test differs from the z-test in which way?

• There are no parameter values involved in a t-test.
• The t-test is more sensitive to minor differences between sample and population.
• With the t-test one can be confident of the normality of the data.
• The t-test requires no parameter standard error of the mean.

5. If experience (yes/no) and department are independent variables in an analysis of productivity, where would variance related to experience have emerged beforehand?

• It wouldn't have been present.
• It would have emerged as an interactive effect.
• It would have been error variance.
• It would have been part of the SSbet..

6. How do statistical tests like the one sample t adjust for the absence of parameter values?

• The values are estimated from sample data.
• The values are assumed to have a constant value.
• The test is reconstructed so that the values aren't needed.
• The test is reformulated so that data are always normal.

7. What happens when independent variables interact?

• Their independent effects become the point of focus.
• They have a combined effect.
• Their effects become unpredictable.
• Their effects cannot be analyzed by an ANOVA model.