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Intuitive Biostatistics

A Nonmathematical Guide to Statistical Thinking

9780190643560
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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. Withan 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.
Product Details
OUP USA
103952
9780190643560
9780190643560

Data sheet

Publication date
2017
Issue number
1
Cover
paperback
Pages count
608
Dimensions (mm)
156 x 235
Weight (g)
757
  • 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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