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Print Price: $143.99

Format:
Paperback
608 pp.
6.125" x 9.25"

ISBN-13:
9780190643560

Copyright Year:
2018

Imprint: OUP US

Intuitive Biostatistics

A Nonmathematical Guide to Statistical Thinking, Fourth Edition

Harvey Motulsky

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

Readership : Undergraduate and graduate students either in biostatistics courses or courses with a biostatistics component. Intuitive Biostatistics can be used either as a primary or a supplementary text.

Reviews

"This splendid book meets a major need in public health, medicine, and biomedical research training. It is a user-friendly biostatistics text for non-mathematicians that clearly explains how to make sense of statistical results, avoid common mistakes in data analysis, avoid being confused by statistical nonsense, and how to make research more reproducible. Students may enjoy statistics for the first time!"
--Gilbert S. Omenn, University of Michigan
"This book applies what I would call 'scientific common sense' to the confusing world of statistical analysis and interpretation. If you want to really understand what a p-value is, read this book."
--Louis G. Zachos, University of Mississippi
"Intuitive Biostatistics places statistical concepts and practical issues of data analysis within an understandable light. The textbook helps the reader grasp the fundamentals and the pitfalls of data presentation and analysis. It should be on the 'must-read' list of clinicians, journal reviewers, editors, and other consumers of scientific literature."
--John D. Bonagura, The Ohio State University
"I have already recommend the book to many colleagues. A concise, well written, and at times funny book that clearly explains the most important conceptual aspects about statistics, emphasizing proper interpretation of results and common mistakes to avoid."
--Walter E. Schargel, The University of Texas at Arlington

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

Instructor Resources
- Lecture notes
- PowerPoint slides
E-Book ISBN 9780190643591

Harvey Motulsky is the CEO and Founder of GraphPad Software, Inc. He wrote the first edition of this book while on the faculty of the Department of Pharmacology at University of California, San Diego.

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Special Features

Nonmathematical approach to statistics focuses on how to interpret statistical results rather than on how to analyze data.
Accessible introduction to the subject for students with no background in biostatistics.
Emphasis on how to avoid common mistakes ensures students are aware of statistical traps to avoid.
End-of-chapter summaries list the most important concepts of each chapter.

New to this Edition

Two new chapters on the reproducibility of scientific findings (Ch. 47) and reporting statistical methods and results (Ch. 48).
Two fully revised chapters on choosing a sample size (Ch. 26) and case-control studies (Ch. 28) provide students with up-to-date coverage of these key topics.
Expanded Question & Answer sections in each chapter provide answers to common questions that students have when studying statistics.
Added coverage of conditional probability.