Comprehending Behavioral Statistics

Introduction: inductive statements; statistical reasoning; rational decision making; a classic example; samples from populations; probability; a note to the student; exercises for Chapter 1. Mathematical concepts: levels of measurement; continuous and discrete variables; summation; exercises for Chapter 2. Frequency distributions: distributions as tables; distributions as graphs; eyeball-estimation; the shape of distributions; eyeball-calibration; bar graphs of nominal and ordinal variables; connections; exercises for Chapter 3. Stem and leaf displays (Optional). Measures of central tendency: mode; median; mean; comparing the mode, median, and Mean; computing the population mean; connections; exercises for Chapter 4. Computing means from frequency distributions (Optional); Measures of variation: range; standard deviation; variance; eyeball-calibration for distributions; connections; exercises for Chapter 5. Computing the standard deviation from frequency distributions (Optional); Using frequency distributions: points in distributions; areas under normal distributions; other standardized distributions based on Z scores; relative frequencies of real-world normal variables; percentiles and percentile rank in normal distributions; connections; exercises for Chapter 6. Linear interpolation (Optional). Samples and the sampling distribution of the means: random samples; the sampling distribution of the means; connections; exercises for Chapter 7. Parameter estimation: point-estimation; distribution of sample means. confidence intervals; connections; exercises for Chapter 8. Evaluating hypotheses: descriptive versus inferential statistics; evaluating hypotheses; the procedure for evaluating hypotheses; connections; exercises for Chapter 9. Why statistical significance alone is not enough (Optional). Inferences about means of single samples: evaluating hypotheses about means; the relationship between hypothesis testing and confidence intervals; one-sample t test eyeball-calibration; statistical significance is not necessarily practical significance; connections; exercises for Chapter 10. eyeball-estimating one-sample t tests; (Optional). Inferences about means of two independent samples: hypotheses with two independent samples; the test statistic; standard error of the difference between two means; testing hypotheses about means of two independent samples; practical significance versus statistical significance revisited; two-sample t test eyeball-calibration; connections; exercises for Chapter 11. eyeball-estimating two-Independent-sample t tests (Optional); Inferences about means of two dependent samples and statistical power of t tests; dependent-sample tests; testing hypotheses about means of two dependent samples; comparing dependent- and independent-sample t tests; dependent-sample t test eyeball-calibration; statistical power; factors that increase power; using power to determine sample size.