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* Read * Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition by Harvey Motulsky ✓ eBook or Kindle ePUB. Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition Error Bars PART D: P Values and Significance 15. Testing for Equivalence or NoninferiorityPART E: Challenges in Statistics 22. Confidence Interval of a Mean 13. Statistical Traps to Avoid46. Choosing a Sample SizePART F: Statistical Tests 27. Nonlinear Regression37. The Theory of Confidence Intervals14. Statistics and Probability Are Not Intuitive 2. Interpreting a Result That Is Not Statistically Significant 20. Review Problems 4
| Title | : | Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition |
| Author | : | |
| Rating | : | 4.59 (727 Votes) |
| Asin | : | 0199946647 |
| Format Type | : | paperback |
| Number of Pages | : | 576 Pages |
| Publish Date | : | 2016-12-24 |
| Language | : | English |
Fantastic review for researchers Fantastic review for researchers. Great explanations for how to approach analyzing different types of data with the most focus being on how to avoid making common mistakes. Absolutely fantastic and practical. I wish my undergrad stats class had been more like this book.. A must-have for people who read research articles PerpetualLearner I used this as a supplementary book to my statistics textbook and it has the most thorough explanations. It helps you really understand the material--not just the "how?", but also the "why?".It was the second favorite supplementary book I used.My most useful supplementary books for statistics class (in order of usefulness) were:1) Statistics in Plain English: clearest explanations2) Intuitive Biostatistics: most thorough explanations3) Statistics in a Nutshell: good summaries for reviewing for tests4) What is p-value anyway?: nice stories but not in-depth en. An excellent statistics book Matt Carter For over a decade, I have been searching for a clear, lucid guide to statistics that I can use in my research and share with my students. Finally, after combing through dozens of books, I can say I found an excellent book.Harvey Motulsky seems to have pulled off the trick of writing a book with high explanatory power that will not intimidate the busy undergraduate, graduate student, postdoc, or primary investigator who wants to learn the necessary information but does not want to drown in esoteric details, problem sets, or unhelpful information. As a practic
Unlike other statistics texts I have seen, it includes extensive and carefully crafted discussions of the perils of multiple comparisons, warnings about common and avoidable mistakes in data analysis, a review of the assumptions that apply to various tests, an emphasis on confidence intervals rather than P values, explanations as to why the concept of statistical significance is rarely needed in scientific work, and a clear explanation of nonlinear regression (commonly used in labs; rarely explained in statistics books).
Error Bars PART D: P Values and Significance 15. Testing for Equivalence or NoninferiorityPART E: Challenges in Statistics 22. Confidence Interval of a Mean 13. Statistical Traps to Avoid46. Choosing a Sample SizePART F: Statistical Tests 27. Nonlinear Regression37. The Theory of Confidence Intervals14. Statistics and Probability Are Not Intuitive 2. Interpreting a Result That Is Not Statistically Significant 20. Review Problems 48. Multiple Comparisons Concepts 23. Nonparametric Methods42. Introducing P Values 16. Confidence Interval of Survival Data 6. Simple Linear Regression34. Graphing Continuous Data 8. Confidence Interval of a Proportion 5. The Lognormal Distribution an
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