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