**Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking 4th Edition by Harvey Motulsky, ISBN-13: 978-0190643560**

[PDF eBook eTextbook]

- Publisher: Oxford University Press; 4th edition (November 15, 2017)
- Language: English
- 608 pages
- ISBN-10: 0190643560
- ISBN-13: 978-0190643560

** Intuitive Biostatistics** takes a non-technical, non-quantitative approach to statistics and emphasizes interpretation of statistical results rather than the computational strategies for generating statistical data. This makes the text especially useful for those in

**health-science**fields who have not taken a biostatistics course before. The text is also an excellent resource for professionals in labs, acting as a conceptually oriented and accessible biostatistics guide. With an engaging and conversational tone,

**provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists.**

*Intuitive Biostatistics***Table of Contents:**

Part A. Introducing Statistics

1. Statistics and Probability are not Intuitive

2. The Complexities of Probability

3. From Sample to Population

Part B. Introducing Confidence Intervals

4. Confidence Interval of a Proportion

5. Confidence Interval of Survival Data

6. Confidence Interval of Counted Data (Poisson Distribution)

Part C. Continuous Variables

7. Graphing Continuous Data

8. Types of Variables

9. Quantifying Scatter

10. The Gaussian Distribution

11. The Lognormal Distribution and Geometric Mean

12. Confidence Interval of a Mean

13. The Theory of Confidence Intervals

14. Error Bars

Part D. P Values and Statistical Significance

15. Introducing P Values

16. Statistical Significance and Hypothesis Testing

17. Comparing Groups with Confidence Intervals and P Values

18. Interpreting a Result That Is Statistically Significant

19. Interpreting a Result That Is Not Statistically Significant

20. Statistical Power

21. Testing For Equivalence or Noninferiority

Part E. Challenges in Statistics

22. Multiple Comparisons Concepts

23. The Ubiquity of Multiple Comparisons

24. Normality Tests

25. Outliers

26. Choosing a Sample Size

Part F. Statistical Tests

27. Comparing Proportions

28. Case-Control Studies

29. Comparing Survival Curves

30. Comparing Two Means: Unpaired t Test

31. Comparing Two Paired Groups

32. Correlation

Part G. Fitting Models to Data

33. Simple Linear Regression

34. Introducing Models

35. Comparing Models

36. Nonlinear Regression

37. Multiple Regression

38. Logistic and Proportional Hazards Regression

Part H. The Rest of Statistics

39. Analysis of Variance

40. Multiple Comparison Tests after ANOVA

41. Nonparametric Methods

42. Sensitivity, Specificity, and Receiver-Operating Characteristic Curves

43. Meta-Analysis

Part I. Putting It All Together

44. The Key Concepts of Statistics

45. Statistical Traps to Avoid

46. Capstone Example

47. Statistics and Reproducibility

48. Checklists for Reporting Statistical Methods and Results

Part J. Appendices

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