This book provides a new approach to statistics in plain English, and walks readers through basic concepts, as well as some of the most complex statistical models in use. The Second Edition contains new chapters on "big data" on the one hand, and on small data situations at the other end of the spectrum; on missing data; and on effect sizes. The two "characters" in the text (a high school principal, and a director of public health) return in the Second Edition, and their needs to make sense of the data available to them have been updated with reference to contemporary concerns in the fields of education and health.
Dana K. Keller, PhD, has explored Eastern philosophies for almost five decades, including a journey to China and Tibet. He embraces two very different worlds: the West's scientific approach to knowledge and the East's more intuitive and experiential approach. In The Tao of Statistics, he presents a way that the two worlds can mutually benefit. After supervising the research for over 100 doctoral dissertations, he joined the Delmarva Foundation as its chief statistician. During his seven years there, the Centers for Medicare & Medicaid Services named him as a national resource for the nation's managed care organizations for sampling and research methodology. His almost unique ability to explain statistical and methodological constructs in everyday language has resulted in his being frequently requested as a presenter and technical expert panel member. As president of Halcyon Research, Inc., he continues to bring his ability to explain statistical concepts simply to an ever-widening audience.
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 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 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 Regressions 40. Abundance - Multivariate Analysis 41. Differences - t Tests and Analysis of Variance 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
This Second Edition retains all the charm and conciseness which made the original so tremendously helpful, and even enjoyable to read. I would not hesitate to recommend it to any student struggling to comprehend statistics. The Tao of Statistics' key benefit lies in its ability to overcome the anxiety which numbers provoke for too many beginners, or even for those struggling with more advanced statistical methodologies. The succinct, single-focus chapters compel readers, by asking that they just understand the intent and usefulness of each aspect, and no more. Thus, it maintains focus on the individual components rather than allowing them to become overwhelmed by the whole. One might say the individual trees can be appreciated once fear of the dark forest is allayed. -- Erica Watson-Currie "The Tao of Statistics: A Path to Understanding (With No Math) appears in its second edition to provide a user-friendly guide to statistics that explains what they mean, with a difference - there's no math involved. Most books are loaded with formulas; this is loaded with explanation. The basic concepts are covered with attention to how and why they are applied, offering important keys to understanding. This second edition adds new coverage of 'big data' and its impact and concepts, material contrasting it with 'small data', and discussions on missing data and more. The result is a pick for any interested in more than math formulas." -- The California Bookwatch "'For most people, the concept of statistics begins as a shadowy mathematical nightmare....' The author opens his introduction to the second edition with these words, and undoubtedly many engaged in statistical research would agree. Unfortunately, these persons sometimes find themselves gathering, processing, and interpreting data with a great sense of discomfort as they blindly follow a mathematical procedure without really understanding what the results actually mean. Keller (president, Halcyon Research, Inc.) clearly has this audience in mind. Purists may be startled by a statistics book without numbers, graphs, or formulas, but they should appreciate Keller's brief, insightful discussions designed to clarify each of his fifty topics. Especially helpful are illustrations of how a high school principal, a public health director, or a sociologist might use the concept at hand. In some cases the author even explains why these individuals might not need a particular procedure at all, a refreshingly honest approach that reflects his sense of tao. Of course, statistics without graphs or data is not realistic, but the user of statistics has to have a basic understanding of what is being accomplished and, in that sense, the author has definitely succeeded." -- N. W. Schillow, Lehigh Carbon Community College
