Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

Book Synopsis: Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: 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.

With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics.

CONTENTS

Part A: Introducing Statistics

1. Statistics and Probability Are Not Intuitive
2. The Complexities of Probability
3. From Sample to Population

Part B: Confidence Intervals

1. Confidence Interval of a Proportion
2. Confidence Interval of Survival Data
3. Confidence Interval of Counted Data

Part C: Continuous Variables

1. Graphing Continuous Data
2. Types of Variables
3. Quantifying Scatter
4. The Gaussian Distribution
5. The Lognormal Distribution and Geometric Mean
6. Confidence Interval of a Mean
7. The Theory of Confidence Intervals
8. Error Bars

Part D: P Values and Significance

1. Introducing P Values
2. Statistical Significance and Hypothesis Testing
3. Relationship Between Confidence Intervals and Statistical Significance
4. Interpreting a Result That Is Statistically Significant
5. Interpreting a Result That Is Not Statistically Significant
6. Statistical Power
7. Testing for Equivalence or Noninferiority

Part E: Challenges in Statistics

1. Multiple Comparisons Concepts
2. The Ubiquity of Multiple Comparison
3. Normality Tests
4. Outliers
5. Choosing a Sample Size

Part F: Statistical Tests

1. Comparing Proportions
2. Case-Control Studies
3. Comparing Survival Curves
4. Comparing Two Means: Unpaired t Test
5. Comparing Two Paired Groups
6. Correlation

Part G: Fitting Models to Data

1. Simple Linear Regression
2. Introducing Models
3. Comparing Models
4. Nonlinear Regression
5. Multiple Regression
6. Logistic and Proportional Hazards Regression

Part H: The Rest of Statistics

1. Analysis of Variance
2. Multiple Comparison Tests After ANOVA
3. Nonparametric Methods
4. Sensitivity and Specificity and Receiver-Operator Characteristic Curves
5. Meta-analysis

Part I: Putting It All Together

1. The Key Concepts of Statistics
2. Statistical Traps to Avoid
3. Capstone Example
4. Review Problems
5. Answers to Review Problems