Question

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Concepts to be familiar with: 1. Difference between populations (parameters) and samples (statistics): definitions and notation differences 2. Descriptive v. inferential statistics. What are they? What are their limitations? 3. Independent Variables (IVs) and Dependent Variables (DVs): definitions and how to recognize which is which in a study description 4. Discrete vs. continuous variables, apparent vs. real limits 5. Scales of measurement (N,O,I,R). Know what they are and examples of each. 6. Frequency tables: a. X, f, 2X, proportion, percentiles, cumulative frequency b. calculating means and percentile ranks from frequency data Graphs (histograms, polygons, bar graphs) - When would we use each? Distribution shapes: symmetrical, bimodal, skewed (positively or neg.). Be able to identify them by shape and identify characteristics of them (like where different central tendency values could be found) Measures of central tendency (mean, median, mode) 7. 8. 9. Properties and calculations of each a. b. In what cases would we prefer a certain measure of central tendency? In what cases would a particular measure not be recommended? 10. Variability: a. b. What does it tell us? Standard deviation: How it's computed, the difference between sample and population calculations, and why it's computed differently 11. Z-scores a. b. c. Why do we use them and how do we calculate them? How do we convert scores between an X-distribution and a z-distribution? How do we convert between a z-distribution and a new standardized (k) distribution? 12. Probability and proportions a. Equation: p(event A) number of possible outcomes for A / total possible outcomes b. Independent random sampling: What does it mean? Why do we use it? 13. Unit Normal Table (BRING TO THE TEST) Convert an X-score to a proportion/percentile Convert a proportion/percentile to a z -or X-score a. b. 14. Standard error of the mean Relation to sample size and variability (these are used to calculate SEM, but also a. what does that mean?ie,, what happens to SEM when sample size or variability increase or decrease?) 15. Central Limit Theorem: definition and what it tells us about sampling distributions sample sizes, and how to find probabilities of a sample mean

Answer 1

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