The Tao of Statistics

A Path to Understanding (With No Math)

Dana K. Keller

• Publisher: SAGE Publications, Inc
• Year: 2015
• Print ISBN:
  9781483377926
• Edition: 2
• eBook ISBN:
  9781483377933
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book's two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.

Table of contents

• Cover
• Half Title
• Publisher Note
• Title Page
• Copyright Page
• Contents
• Acknowledgments
• About the Author
• Introduction to the Second Edition
• 1. The Beginning — The Question
• 2. Ambiguity—Statistics
• 3. Fodder—Data
• 4. Data—Measurement
• 5. Data Structure—Levels of Measurement
• 5.A. Nominal
• 5.B. Ordinal
• 5.C. Interval
• 5.D. Ratio
• 6. Simplifying—Groups and Clusters
• 7. Counts—Frequencies
• 8. Pictures—Graphs
• 9. Scatterings—Distributions
• 10. Bell-Shaped—The Normal Curve
• 11. Lopsidedness—Skewness
• 12. Averages—Central Tendencies
• 12.A. Mean
• 12.B. Median
• 12.C. Mode
• 13. Two Types—Descriptive and Inferential
• 14. Foundations—Assumptions
• 15. Murkiness—Missing Data
• 16. Leeway—Robustness
• 17. Consistency—Reliability
• 18. Truth—Validity
• 19. Unpredictability—Randomness
• 20. Representativeness—Samples
• 21. Mistakes—Error
• 22. Real or Not—Outliers
• 23. Impediments—Confounds
• 24. Nuisances—Covariates
• 25. Background—Independent Variables
• 26. Targets—Dependent Variables
• 27. Inequality—Standard Deviations and Variance
• 28. Prove—No, Falsify
• 29. No Difference—The Null Hypothesis
• 30. Reductionism—Models
• 31. Risk—Probability
• 32. Uncertainty—p Values
• 33. Expectations—Chi-Square
• 34. Importance vs. Difference—Substantive vs. Statistical Significance
• 35. Strength—Power
• 36. Impact—Effect Sizes
• 37. Likely Range—Confidence Intervals
• 38. Association—Correlation
• 39. Predictions—Multiple Regression
• 40. Abundance—Multivariate Analysis
• 41. Differences—t Tests and Analysis of Variance
• 41.A. ANOVA
• 41.B. ANCOVA
• 41.C. MANOVA
• 41.D. MANCOVA
• 42. Differences That Matter—Discriminant Analysis
• 43. Both Sides Loaded—Canonical Covariance Analysis
• 44. Nesting—Hierarchical Models
• 45. Cohesion—Factor Analysis
• 46. Ordered Events—Path Analysis
• 47. Digging Deeper—Structural Equation Models
• 48. Abundance—Big Data
• 49. Scarcity—Small Data
• 50. Fiddling—Modifications and New Techniques
• 51. Epilogue
• Publisher Note