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Cover

Intuitive Biostatistics

A Nonmathematical Guide to Statistical Thinking

Fourth Edition

Harvey Motulsky

Publication Date - November 2017

ISBN: 9780190643560

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

In Stock

Retail Price to Students: $99.99

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

Description

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.

New to this Edition

  • Two new chapters: Chapter 47, "Statistics and Reproducibility," and Chapter 48, "Checklists for Reporting Statistical Methods and Results"
  • Two completely revised chapters: Chapter 26, "Choosing a Sample Size," and Chapter 28, "Case-Control Studies"
  • Every chapter edited for clarity and updated with current material
  • Improved "Q & A" and "Common Mistakes" sections throughout the text

Features

  • Focuses on how to interpret statistical results, rather than how to analyze data
  • Written for students and scientists who find statistical mathematics confusing and prefer verbal explanations
  • Shows how common sense can mislead when considering statistical results
  • Covers a variety of approaches used to deal with multiple comparisons
  • Gives equal weight to linear and nonlinear regression
  • End-of-chapter summaries: each chapter now ends with a list of the most important points for students to remember

About the Author(s)

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

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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