Chapter 9: Inference with the Normal Curve

1. What is the purpose of interval estimation and hypothesis testing?
2. What is the definition of a confidence interval?
3. Identify the 2 formulas for setting up the 95% and 99% confidence intervals for the population mean.
4. In the random sampling distribution (RSD) of the mean, 95% of the sample means fall within ____ standard deviations from the mean and ___% of the means fall within +/- 2.58 standard deviations from the mean of this distribution.
5. Identify the 2 formulas for setting up the 95% and 99% CI for the difference between means.
6. Describe the different types of hypotheses used in hypothesis testing and give an example of each.
7. What is the difference between a null and alternative hypothesis?
8. Give an example of a directional and non-directional Ha.
9. What is the purpose of significance levels?
10. Why are critical Z values important in hypothesis testing?
11. What is the region of rejection and non-rejection according to the normal curve?
12. What is the difference between a one- tailed and two-tailed test of significance?
13. Critical Z values: =.05 =.01

Two-tailed test ____________ ____________

One-tailed test ____________ ____________

1. Why are the critical Z values smaller in a one-tailed test of significance?
2. What is the difference between a Type l and Type ll error?
3. What can we do to reduce the probability of making a Type 1 error?
4. How does increasing sample size affect ?
5. The probability of rejecting a false null hypothesis is called__________.
6. Variation in sample means drawn from a single population is referred to as ______________________.
7. Name 4 factors that influence the power of any statistical test of significance

Chapter 10: Inference with the t - Distribution

1. Name 3 shared features between the normal and the t-distribution.
2. When do we use the t-distribution?
3. What is the difference between a Z-score and a t-score?
4. What is an unbiased estimate of a parameter?
5. How does a t-distribution differ in shape from the normal distribution?
6. Define degrees of freedom and explain how they affect the shape of the t-distribution.
7. Identify the formula for setting up the confidence interval for the population mean in a t-distribution.
8. Identify the formula for setting up the confidence interval for the difference between independent population means in a t-distribution.
9. Provide the 2 formulas used to find the standard error of the difference between independent population means. What formulas would we use to find the degrees of freedom when n1 =n2 and n1 n2.
10. Present the following data in the proper notation: With 99% confidence, the mean difference between males and females on a test of creativity lies between -3.67 and -0.96. ________________________________________.
11. List the basic assumptions when making inferences about population parameters in the t-distribution.
12. Define homogeneity of variance.
13. What can we assume when subjects have been randomly assigned to each group in an experiment?
14. How does a within-subjects design influence between-subjects variability?
15. What is a matched groups design?
16. From a mathematical standpoint, what happens to the standard error when there is little variability between subjects? What happens to the t-value?
17. What are some advantages and disadvantages of using dependent groups designs?
18. Describe the direct difference method. What is the formula for finding degrees of freedom?
19. Name 3 steps you can take to produce a powerful test.

20. Identify the steps that you would take in choosing an appropriate test of significance.

Chapter 11: Inference with the F Distribution

1. What is ANOVA? When do we use ANOVA as an inferential technique?
2. What are the characteristics of the F distribution?
3. Why do we not distinguish between directional and non-directional alternative hypotheses in ANOVA?
4. Describe the 3 measures of variability in ANOVA.
5. Why do we compute three separate sums of squares in ANOVA?
6. Identify the computational formulas for SSTOT and SSBG.
7. Dividing each SS by its associated degrees of freedom produces unbiased estimates of the variance called______________ in ANOVA.
8. How do we compute degrees of freedom for SSTOT, SSBG, and SSWG?
9. Identify the formulas for computing MSBGand MSWGfor a one-way ANOVA.
10. If the treatment in an experiment worked, then we would expect that the MSBGwould be ________ than the MSWGbecause it reflects not only inherent variation, but also treatment effects.
11. The F-ratio is a ratio between what two estimates?
12. What is the critical F value at = .05 when the degrees of freedom for SSBG =12 and the degrees of freedom for SSWG =50?
13. How does a two-way ANOVA differ from a one-way ANOVA?
14. What is the difference between a main effect and an interaction in a two-way ANOVA?
15. Describe a 2X2 complete factorial design.
16. Describe how the SSBG can be partitioned into 3 sums of squares in a two-way ANOVA.
17. List the formulas for mean squares in a two-way ANOVA.
18. List the 3 F-ratios for a two-way ANOVA.
19. How do we compute the SS for the main effects (A, B) and the interaction (AxB) in a two-way ANOVA?
20. Provide the formulas for degrees of freedom for the main effect of A, B, and the interaction in a two-way ANOVA.
21. What is an experiment-wise error rate? How is it beneficial in ANOVA?