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Cover

Intuitive Biostatistics

A Nonmathematical Guide to Statistical Thinking

Third Edition

Harvey Motulsky

Publication Date - December 2013

ISBN: 9780199946648

576 pages
Paperback
6-1/8 x 9-1/4 inches

In Stock

Retail Price to Students: $67.95

Designed for consumers of statistical data, Intuitive Biostatistics is a non-mathematical guide to statistical thinking

Description

Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes.

With its 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. It is especially useful for those students in health-science related fields who have no background in biostatistics.

New to this Edition

  • New chapter 2: The complexities of probability. Statistical thinking is based on understanding the basic concepts of probability, so this chapter explains the basics.
  • New chapter 43: Meta-analysis. Combining results of many studies, meta-analysis, is becoming more and more common. This chapter helps you understand a published meta-analysis.
  • Emphasis on how to avoid common mistakes. Most chapters have sections on common mistakes, and Chapter 45 has been completely rewritten to focus on statistical traps to avoid.
  • Summary of terminology. Each chapter now ends with a list of terms introduced in that chapter. Much of the challenge in learning statistics is learning the terminology, and these lists will make it easier.
  • More Q&A sections. The question and answer sections of the second edition were popular, so they have been expanded and included in more chapters.
  • End of chapter summaries. Each chapter now ends with a list of the most important points you should remember.
  • More topics. Expanded coverage of new topics, including pseudoreplicates, genome-wide association studies, primary vs. secondary outcomes in clinical trials, researchers degrees of freedom, P-hacking, multiplicity adjusted P values, and more.

About the Author(s)

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

Previous Publication Date(s)

January 2010
October 1995

Reviews

"Intuitive Biostatistics is a beautiful book that has much to teach experimental biologists of all stripes. Motulsky has written thoughtfully, with compelling logic and wit. He teaches by example what one may expect of statistical methods and, perhaps just as importantly, what one may not expect of them. He is to be congratulated for this work, which will surely be valuable and perhaps even transformative for many of the scientists who read it."--Bruce Beutler, 2011 Nobel Laureate, Physiology or Medicine, and Director, Center for the Genetics of Host Defense, UT Southwestern Medical Center

"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 with varying levels of quantitative backgrounds."--Heather Hoffman, George Washington University

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 Significance
    15. Introducing P Values
    16. Statistical Significance and Hypothesis Testing
    17. Relationship Between Confidence Intervals and Statistical Significance
    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. Review Problems
    Answers to Review Problems