1. Basic Concepts. Important Terms. Descriptive and Inferential Statistics. Measurement Scales. Using Computers. The Plan of the Book. 2. Describing And Exploring Data. Plotting Data. Histograms. Stem-and-Leaf Displays. Alternative Methods of Plotting Data. Describing Distributions. Using Computer Programs to Display Data. Notation. Measures of Central Tendency. Measures of Variability. BoxPlots: Graphical Representations of Dispersions and Extreme Scores. Obtaining Measures of Dispersion Using Minitab. Percentiles, Quartiles, and Deciles. The Effect of Linear Transformations on Data. 3. The Normal Distribution. The Normal Distribution. The Standard Normal Distribution. Using the Tables of the Standard Normal Distribution. Setting Probable Limits on an Observation. Measures Related to z. 4. Sampling Distributions And Hypothesis Testing. Two Simple Experiments Involving Course Evaluations and Rude Motorists. Sampling Distributions. Hypothesis Testing. The Null Hypothesis. Test Statistics and Their Sampling Distributions. Using the Normal Distribution to Test Hypotheses. Type I and Type II Errors. One- and Two-Tailed Tests. What Does It Mean to Reject the Null Hypothesis? Effect Size. A Final Worked Example. Back to Course Evaluations and Rude Motorists. 5. Basic Concepts Of Probability. Probability. Basic Terminology and Rules. Discrete Versus Continuous Variables. Probability Distributions for Discrete Variables. Probability Distributions for Continuous Variables. Permutations and Combinations. The Binomial Distribution. Using the Binomial Distribution to Test Hypotheses. The Multinomial Distribution. 6. Categorical Data And Chi-Square. The Chi-Square Distribution. Statistical Importance of the Chi-Square Distribution. The Chi-Square Goodness-of-Fit Test-One-Way Classification. Two Classification Variables: Contingency Table Analysis. Chi-Square for Larger Contingency Tables. Chi-Square for Ordinal Data. Summary of the Assumptions of Chi-Square. One- and Two-Tailed Tests. Likelihood Ratio Test. Measures of Association. 7. Hypothesis Tests Applied To Means. Sampling Distribution of the Mean. Testing Hypotheses about Means - s Known. Testing a Sample Mean When s is Unknown -The One-Sample t test. Hypothesis Tests Applied to Means - Two Matched Samples. Hypothesis Tests Applied to Means - Two Independent Samples. Confidence Intervals. A Second Worked Example. Heterogeneity of Variance: The Behrens-Fisher Problem. 8. Power. Factors Affecting the Power of a Test. Effect Size. Power Calculations for the One-Sample t. Power Calculations for Differences Between Two Independent Means. Power Calculations for Matched-Sample t. Power Considerations in Terms of Sample Size. Post-Hoc Power. 9. Correlation And Regression. Scatterplot. The Relationship Between Stress and Health. The Covariance. The Pearson Product-Moment Correlation Coefficient (r). The Regression Line. The Accuracy of Prediction. Assumptions Underlying Regression and Correlation. Confidence Limits on Y. A Computer Example Showing the Role of Test-Taking Skills. Hypothesis Testing. The Role of Assumptions in Correlation and Regression. Factors That Affect the Correlation. Power Calculation for Pearson''s r. 10. Alternative Correlational Techniques. Point-Biserial Correlation and PHI: Pearson Correlation by Another Name. Biserial and Tetrachoric Correlation: Non-Pearson Correlation Coefficients. Correlation Coefficients for Ranked Data. Analysis of Contingency Tables with Ordered Variables. Kendall''s Coefficient of Concordance (W). 11. Simple Analysis Of Variance. An Example. The Underlying Model. The Logic of the Analysis of Variance. Calculations in the Analysis of Variance. Computer Solutions. Derivation of the Analysis of Variance. Unequal Sample Sizes. Violations of Assumptions. Transformations. Fixed Versus Random Models. Magnitude of Experimental Effect. Power. Computer Analyses. 12. Multiple Comparisons Among Treatment Means. Error Rates. Multiple Comparisons in a Simpl