Description / Table of Contents:
"Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, 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: * Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. * Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. * Chapter 21 contrasts testing for equivalence with testing for differences. * Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. * Chapters 24 and 25 review testing for normality and outliers. * Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. * Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. * Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. * Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. * New "Q and A" sections throughout the book review key concepts"--Provided by publisher.
Type of Medium:
1 v. (various pagings) :
Completely rev. 2nd ed.
9780199730063 (pbk. : alk. paper)
Machine generated contents note: -- Preface PART A. INTRODUCING STATISTICS 1. Statistics and Probability Are Not Intuitive 2. Why Statistics Can Be Hard to Learn 3. From Sample to Population PART B. CONFIDENCE INTERVALS 4. Confidence Interval of a Proportion 5. Confidence Interval of Survival Data 6. Confidence Interval of Counted Data 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 SIGNIFICANCE 15. Introducing P Values 16. Statistical Significance and Hypothesis Testing 17. Relationship Between Confidence Intervals and Statistical Significance 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. Multiple Comparison Traps 24. Gaussian or Not? 25. Outliers PART F. STATISTICAL TESTS 26. Comparing Observed and Expected Distributions 27. Comparing Proportions: Prospective and Experimental Studies 28. Comparing Proportions: 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. Models 35. Comparing Models 36. Nonlinear Regression 37. Multiple, Logistic, and Proportional Hazards Regression 38. Multiple Regression Traps PART H. THE REST OF STATISTICS 39. Analysis of Variance 40. Multiple Comparison Tests After ANOVA 41. Nonparametric Methods 42. Sensitivity Specificity and Receiver-Operator Characteristic Curves 43. Sample Size PART I. PUTTING IT ALL TOGETHER 44. Statistical Advice 45. Choosing a Statistical Test 46. Capstone Example 47. Review Problems 48. Answers to Review
Problems APPENDICES A. Statistics with GraphPad B. Statistics With Excel C. Statistics R D. Values of the t Distribution Needed to Compute CIs E. A Review of Logarithms References.