
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
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 â The Question
- 2. AmbiguityâStatistics
- 3. FodderâData
- 4. DataâMeasurement
- 5. Data StructureâLevels of Measurement
- 5.A. Nominal
- 5.B. Ordinal
- 5.C. Interval
- 5.D. Ratio
- 6. SimplifyingâGroups and Clusters
- 7. CountsâFrequencies
- 8. PicturesâGraphs
- 9. ScatteringsâDistributions
- 10. Bell-ShapedâThe Normal Curve
- 11. LopsidednessâSkewness
- 12. AveragesâCentral Tendencies
- 12.A. Mean
- 12.B. Median
- 12.C. Mode
- 13. Two TypesâDescriptive and Inferential
- 14. FoundationsâAssumptions
- 15. MurkinessâMissing Data
- 16. LeewayâRobustness
- 17. ConsistencyâReliability
- 18. TruthâValidity
- 19. UnpredictabilityâRandomness
- 20. RepresentativenessâSamples
- 21. MistakesâError
- 22. Real or NotâOutliers
- 23. ImpedimentsâConfounds
- 24. NuisancesâCovariates
- 25. BackgroundâIndependent Variables
- 26. TargetsâDependent Variables
- 27. InequalityâStandard Deviations and Variance
- 28. ProveâNo, Falsify
- 29. No DifferenceâThe Null Hypothesis
- 30. ReductionismâModels
- 31. RiskâProbability
- 32. Uncertaintyâp Values
- 33. ExpectationsâChi-Square
- 34. Importance vs. DifferenceâSubstantive vs. Statistical Significance
- 35. StrengthâPower
- 36. ImpactâEffect Sizes
- 37. Likely RangeâConfidence Intervals
- 38. AssociationâCorrelation
- 39. PredictionsâMultiple Regression
- 40. AbundanceâMultivariate Analysis
- 41. Differencesât Tests and Analysis of Variance
- 41.A. ANOVA
- 41.B. ANCOVA
- 41.C. MANOVA
- 41.D. MANCOVA
- 42. Differences That MatterâDiscriminant Analysis
- 43. Both Sides LoadedâCanonical Covariance Analysis
- 44. NestingâHierarchical Models
- 45. CohesionâFactor Analysis
- 46. Ordered EventsâPath Analysis
- 47. Digging DeeperâStructural Equation Models
- 48. AbundanceâBig Data
- 49. ScarcityâSmall Data
- 50. FiddlingâModifications and New Techniques
- 51. Epilogue
- Publisher Note