Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking by Harvey Motulsky

Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking

byHarvey Motulsky

Paperback | December 18, 2013

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With an engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. Designed for consumers of statistical data, IntuitiveBiostatistics is a non-mathematical guide to statistical thinking using minimal equations and including a detailed review of assumptions and common mistakes.

About The Author

Harvey Motulsky is the founder and CEO of GraphPad Software, Inc. The first edition of this book was written while he was on the faculty of the Department of Pharmacology at University of California, San Diego.

Details & Specs

Title:Intuitive Biostatistics: A Nonmathematical Guide to Statistical ThinkingFormat:PaperbackDimensions:544 pages, 9.25 × 6.12 × 0.68 inPublished:December 18, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199946647

ISBN - 13:9780199946648

Customer Reviews of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking


Extra Content

Table of Contents

PART A. Introducing Statistics1. Statistics and Probability Are Not Intuitive2. The Complexities of Probability3. From Sample to PopulationPART B. Introducing Confidence Intervals4. Confidence Interval of a Proportion5. Confidence Interval of Survival Data6. Confidence Interval of Counted Data (Poisson Distribution)PART C. Continuous Variables7. Graphing Continuous Data8. Types of Variables9. Quantifying Scatter10. The Gaussian Distribution11. The Lognormal Distribution and Geometric Mean12. Confidence Interval of a Mean13. The Theory of Confidence Intervals14. Error BarsPART D. P Values and Significance15. Introducing P Values16. Statistical Significance and Hypothesis Testing17. Relationship Between Confidence Intervals and Statistical Significance18. Interpreting a Result That Is Statistically Significant19. Interpreting a Result That Is Not Statistically Significant20. Statistical Power21. Testing for Equivalence or NoninferiorityPART E. Challenges in Statistics22. Multiple Comparisons Concepts23. The Ubiquity of Multiple Comparisons24. Normality Tests25. Outliers26. Choosing a Sample SizePART F. Statistical Tests27. Comparing Proportions28. Case-control studies29. Comparing Survival Curves30. Comparing Two Means: Unpaired t Test31. Comparing Two Paired Groups32. CorrelationPART G. Fitting Models to Data33. Simple Linear Regression34. Introducing Models35. Comparing Models36. Nonlinear Regression37. Multiple Regression38. Logistic and Proportional Hazards RegressionPART H. The Rest of Statistics39. Analysis of Variance40. Multiple Comparison Tests after ANOVA41. Nonparametric Methods42. Sensitivity, Specificity, and Receiver-Operator Characteristic Curves43. Meta-analysisPART I. Putting It All Together44. The Key Concepts of Statistics45. Statistical Traps to Avoid46. Capstone Example47. Review ProblemsAnswers to Review Problems

Editorial Reviews

"Let's face it. Most statistics textbooks intimidate the average student. Motulsky's Intuitive Biostatistics, however, is written in a welcoming tone. It takes the static out of statistics. This textbook covers a wide spectrum of statistical concepts in a way that will benefit readers withvarying levels of quantitative backgrounds." --Heather Hoffman, George Washington University