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This readable revision is presented as simply as possible to ensure that readers will gain a solid understanding of statistical procedures. The goal of this book is to demystify statistics. Presented with rules of evidence and the logic behind those rules. The book is divided into three major units: Descriptive Statistics, Inferential Statistics, and Advanced Topics in Inferential Statistics. Includes definitions of key concepts in the glossary to prevent confusion over terminology. Presents a brief biography of six people who have had the most impact on the field of statistics. Offers new and easier methods for finding percentiles in Chapter 5. For anyone interested in Social Statistics and Measurement.

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• Unit 1 Descriptive Statistics
• 1 Introduction to Statistics
• Stumbling Blocks to Statistics
• A Brief Look at the History of Statistics
• Benefits of a Course in Statistics
• General Field of Statistics
• 2 Graphs and Measures of Central Tendency
• Graphs
• Measures of Central Tendency
• Appropriate Use of the Mean, the Median, and the Mode
• 3 Variability
• Measures of Variability
• Graphs and Variability
• Questionnaire Percentages
• 4 The Normal Curve and z Scores
• The Normal Curve. z Scores
• Translating Raw Scores into z Scores. z Score Translations in Practice
• Fun with Your Calculator
• 5 z Scores Revisited: t Scores and Other Normal Curve Transformations
• Other Applications of the z Score
• The Percentile Table. t Scores
• Normal Curve Equivalents
• Stanines
• Grade-Equivalent Scores: A Note of Caution
• The Importance of the z Score
• 6 Probability
• The Definition of Probability
• Probability and Percentage Areas of the Normal Curve
• Combining Probabilities for Independent Events
• A Reminder about Logic
• Unit 2 Inferential Statistics
• 7 Statistics and Parameters
• Generalizing from the Few to the Many
• Key Concepts of the Inferential Statistics
• Techniques of Sampling
• Exit Polling
• Sampling Distributions
• Back to z
• Some Words of Encouragement
• 8 Parameter Estimates and Hypothesis Testing
• The Standard Deviation Revisited
• Estimating the Standard Error of the Mean
• Estimating the Popular Mean: Interval Estimates and Hypothesis Testing
• The t ratio
• The Type 1 Error
• Alpha Levels
• Effect Size
• Interval Estimates: No Hypothesis Needed
• 9 The Fundamentals of Research Methodology
• Research Strategies
• Independent and Dependent Variables
• The Cause-and-Effect Trap
• Theory of Measurement
• Research: Experimental versus Post-Facto
• The Experimental Method: The Case of Cause and Effect
• Creating Equivalent Groups: The True Experiment
• Designing the True Experiment
• The Hawthorne Effect
• Repeated Measure Designs with Separate Control Groups
• Requirements for the True Experiment
• Post-Facto Research
• Combination Research
• Research Errors
• Experimental Error: Failure to Use an Adequate Control Group
• Post-Facto Errors
• Meta-Analysis
• Methodology as a Basis for More Sophisticated Techniques
• 10 The Hypothesis of Difference
• Sampling Distribution of Differences
• Estimated Standard Error of Difference
• Two-Sample t Test for Independent Samples
• Significance
• Two-Tail t Table
• Alpha and Confidence Levels
• Confidence Interval for Differences Between Two Independent Samples
• The Minimum Difference
• Outliers
• One-Tail t Test
• Importance of Having at Least Two Samples
• Power
• Effect Size
• 11 The Hypothesis of Association: Correlation
• Cause and Effect
• The Pearson r
• Interclass versus Intraclass
• Correlation Matrix
• The Spearman r
• An Important Difference between Correlation Coefficient and the t Test
• 12 Analysis of Variance
• Advantages of ANOVA
• Analyzing the Variance
• Applications of ANOVA
• The Factorial ANOVA
• Eta squared and d