**Inferential Statistics complete solutions correct answers key**

2.What are degrees of freedom? How are they calculated?

3.What do inferential statistics allow you to infer?

4.What is the General Linear Model (GLM)? Why does it matter?

5.Compare and contrast parametric and nonparametric statistics. Why and in what types of cases would you use one over the other?

6.Why is it important to pay attention to the assumptions of the statistical test? What are your options if your dependent variable scores are not normally distributed?

**NHST**

1.What does p = .05 mean? What are some misconceptions about the meaning of p =.05? Why are they wrong? Should all research adhere to the p = .05 standard for significance? Why or why not?

2.Compare and contrast the concepts of effect size and statistical significance.

3.What is the difference between a statistically significant result and a clinically or "real world" significant result? Give examples of both.

4.What is NHST? Describe the assumptions of the model.

5.Describe and explain three criticisms of NHST.

6.Describe and explain two alternatives to NHST. What do their proponents consider to be their advantages?

7.Which type of analysis would best answer the research question you stated in Activity 1? Justify your answer.

**F Ratio**

2.What is an F-ratio? Define all the technical terms in your answer.

3.What is error variance and how is it calculated?

4.Why would anyone ever want more than two (2) levels of an independent variable?

5.If you were doing a study to see if a treatment causes a significant effect, what would it mean if within groups, variance was higher than between groups variance? If between groups variance was higher than within groups variance? Explain your answer

6.What is the purpose of a post-hoc test with analysis of variance?

7.What is probabilistic equivalence? Why is it important?

**Experimental Designs**

2.Explain the difference between multiple independent variables and multiple levels of independent variables. Which is better?

3.What is blocking and how does it reduce "noise"? What is a disadvantage of blocking?

4.What is a factor? How can the use of factors benefit a design?

5.Explain main effects and interaction effects.

6.How does a covariate reduce noise?

7.Describe and explain three trade-offs present in experiments

**Quasi Experimental Designs**

2.Describe the advantages and disadvantages of quasi-experiments? What is the fundamental weakness of a quasi-experimental design? Why is it a weakness? Does its weakness always matter?

3.If you randomly assign participants to groups, can you assume the groups are equivalent at the beginning of the study? At the end? Why or why not? If you cannot assume equivalence at either end, what can you do? Please explain.

4.Explain and give examples of how the particular outcomes of a study can suggest if a particular threat is likely to have been present.

5.Describe each of the following types of designs, explain its logic, and why the design does or does not address the selection threats discussed in Chapter 7 of Trochim and Donnelly (2006):

a.Non-equivalent control group pretest only

b.Non-equivalent control group pretest/posttest

c.Cross-sectional

d.Regression-Discontinuity

6.Why are quasi-experimental designs used more often than experimental designs?

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