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Essential Biostatistics

A Nonmathematical Approach

Harvey Motulsky

Publication Date - June 2015

ISBN: 9780199365067

208 pages
6-1/8 x 9-1/4 inches

In Stock

A concise and affordable nonmathematical guide to statistical thinking


With its engaging and conversational tone, Essential Biostatistics: A Nonmathematical Approach provides a clear introduction to statistics for students in a wide range of fields, and a concise statistics refresher for scientists and professionals who need to interpret statistical results. It explains the ideas behind statistics in nonmathematical terms, offers perspectives on how to interpret published statistical results, and points out common conceptual traps to avoid. It can be used as a stand-alone text or as a supplement to a traditional statistics textbook.

About the Author(s)

Harvey Motulsky is the founder and CEO of GraphPad Software, Inc.


"Essential Biostatistics distills the essence of university-level biostatistics topics in accessible, concise language that is engaging and thought-provoking. This text would be an excellent companion to a traditional biostatistics book."--Derek Webb, Bemidji State University

"The author does a great job explaining why we use statistics rather than getting bogged down explaining how we calculate statistics. I find it refreshing to step back from the calculations to see the larger context of why we use statistics in science."--Dean W. Coble, Stephen F. Austin State University

"I really like the clear and humorous style, the wealth of examples, and the discussions of the limits and pitfalls. This is a wonderful book."--Naji Younes, George Washington University

Table of Contents

    1. Statistics and Probability Are Not Intuitive
    2. The Complexities of Probability
    3. From Sample to Population
    4. Confidence Intervals
    5. Types of Variables
    6. Graphing Variability
    7. Quantifying Variation
    8. The Gaussian Distribution
    9. The Lognormal Distribution and Geometric Mean
    10. Confidence Interval for a Mean
    11. Error Bars
    12. Comparing Groups with Confidence Intervals
    13. Comparing Groups with P Values
    14. Statistical Significance and Hypothesis Testing
    15. Interpreting a Result that Is (Or Is Not) Statistically Significant
    16. How Common Are Type I Errors?
    17. Multiple Comparisons
    18. Statistical Power and Sample Size
    19. Commonly Used Statistical Tests
    20. Normality Tests
    21. Outliers
    22. Correlation
    23. Simple Linear Regression
    24. Nonlinear Regression
    25. Multiple and Logistic Regression
    26. Summary: The Key Concepts of Statistics
    27. Statistical Traps to Avoid