Synopses & Reviews
Designed to provide a nonmathematical introduction to biostatistics for medical and health science students, graduate students in the biological sciences, physicians, and researchers, this text explains statistical principles in non-technical language and focuses on explaining the proper scientific interpretation of statistical tests rather than on the mathematical logic of the tests themselves.
Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression.
By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.
"I like this book much, I will be "beta-testing" it on my upper-level undergraduate biostatistics class. It fills a great need for my students."--Harriette Phelps, University of D.C.
"This splendid book meets a major need in public health, medicine, and biomedical research training--a user-friendly biostatistics text for non-mathematicians."--Gilbert S. Omenn, Executive Vice President for Medical Affairs at the University of Michigan
"Motulsky has written a very readable and delightful account of how statistics are used in biology and medicine...He focuses on clinical studies and covers a broad range of topics, including...such specialized areas as survival analysis, Bayesian inference, and logistic regression." --Quarterly Review of Biology
"The unique aspect of the book, which makes it different from other biostatistics books, is its approach to the content...His goal is to help the reader interpret medical literature rather than analyze a set of data....I higly recommend this book for those needing a non-mathematical, explanatory introduction to biostatistics. It is well-written and provides wonderful clinical examples and biostatistical content...An excellent resource book for medical students and housestaff who are struggling along with the concepts; and for those of you who were wondering, it was surprisingly easy to read."--Joseph Chu, MD, MPH, University of Washington in Teaching and Learning Medicine
This non-mathematical introduction to statistics has been designed for medical students, physicians and researchers in the biological sciences. Providing step-by-step guidelines, it explains how to interpret advanced statistical methods. The text assumes that calculations will be done by computer.
3)Emphasis on confidence intervals over P values. This book presents the concepts of confidence intervals before explaining P values. This makes it easier for the student to understand both.
Table of Contents
1. Introduction to Statistics
I. Confidence Intervals
2. Confidence Interval of a Proportion
3. The Standard Deviation
4. The Gaussian Distribution
5. The Confidence Interval of a Mean
6. Survival Curves
II. Comparing Groups with Confidence Intervals
7. Confidence Interval of a Difference between Means
8. Confidence Interval of the Difference or Ratio of Two Proportions: Prospective Studies
9. Confidence Interval of the Ratio of Two Proportions: Case-Control Studies
III. Introduction to P Values
10. What is a P Value?
11. Statistical Significance and Hypothesis Testing
12. Interpreting Significant and Not Significant P Values
13. Multiple Comparisons
IV. Bayesian Logic
14. Interpreting Lab Tests: Introduction to Bayesian Thinking
15. Bayes and Statistical Significance
16. Bayes' Theorem in Genetics
V. Correlation and Regression
18. An Introduction to Regression
19. Simple Linear Regression
VI. Designing Clinical Studies
20. The Design of Clinical Trials
21. Clinical Trials Where N=1
22. Choosing an Appropriate Sample Size
VII. Common Statistical Tests
23. Comparing Two Groups: Unpaired t Test
24. Comparing Two Means: The Randomization and Mann-Whitney Tests
25. Comparing Two Paired Groups: Paired t and Wilcoxon Tests
26. Comparing Observed and Expected Counts
27. Comparing Two Proportions
VIII. Introduction to Advanced Statistical Tests
28. The Confidence Interval of Counted Variables
29. Further Analyses of Contingency Tables
30. Comparing Three or More Means: Analysis of Variance
31. Multiple Regression
33. Comparing Survival Curves
34. Using Nonlinear Regression to Fit Curves
35. Combining Probabilities
36. Adjusting for Confounding Variables
37. Choosing a Test
38. The Big Picture