Essential Biostatistics: A Nonmathematical Approach

Paperback | July 3, 2015

byHarvey Motulsky

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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. Itexplains 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.

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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. Itexplains the ideas behind statistics in nonma...

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

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Format:PaperbackDimensions:208 pages, 9.25 × 6.12 × 0.68 inPublished:July 3, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199365067

ISBN - 13:9780199365067

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Table of Contents

1. Statistics and Probability Are Not Intuitive2. The Complexities of Probability3. From Sample to Population4. Confidence Intervals5. Types of Variables6. Graphing Variability7. Quantifying variation8. The Gaussian distribution9. The Lognormal Distribution and Geometric Mean10. Confidence Interval for a Mean11. Error Bars12. Comparing Groups with Confidence Intervals13. Comparing Groups with P Values14. Statistical Significance and Hypothesis Testing15. Interpreting a Result That Is (Or Is Not) Statistically Significant16. How common are Type I errors?17. Multiple Comparisons18. Statistical Power and Sample Size19. Commonly Used Statistical Tests20. Normality Tests21. Outliers22. Correlation23. Simple Linear Regression24. Nonlinear Regression25. Multiple and Logistic Regression26. Summary: The Key Concepts of Statistics27. Statistical Traps to AvoidReferencesIndex