Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking

4. Auflage 2017
ISBN: 978-0-19-064356-0
Verlag: OXFORD UNIV PR

Buch, Englisch, 608 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 754 g

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.

Autoren/Hrsg.

Motulsky, Harvey

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

- 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

Über Autor(innen)

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.