Rating: 
Summary: A Good Conceptual Book in Need of Editing
Review: I adopted this book as the text for a one quarter course in introductory biostatistics at UCSD Extension. I like the "spirit" of the book, and feel that it meets the needs of biomedical professionals who are our audience, better than a standard introductory statistics text such as Triola or Freund & Wilson. The stress in the book is placed on conceptual understanding of confidence intervals instead of mechanical computation of p-values.
As a mathematician, however, I was disappointed by the lack of rigor in the book, and especially at the plethora of mistakes, both in the text and in the solutions to the exercises. So one must teach from this book with caution, and use this book with a supplement, such as Schaum's Outline or Cliff's Notes, if one wants students to learn how to do the statistics.
Rating: 
Summary: THE most accessible introduction...
Review: ...to practical statistical methods I've seen. Dr. Motulsky articulately enables consideration and application of statistical methods. Flow is excellent throughout the book, both within and between topics. This is a great reference when using the Graphpad program Prism (also an excellent and equally accessible resource), but it would be an excellent companion to any computer stat program. If you need a handy book, I recommend this one without reservation - it's like a statistical Swiss Army knife.
Rating: 
Summary: Table of Contents
Review: 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
17. Correlation
18. An Introdution 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 IX. Overviews
36. Adjusting for Confounding Variables
37. Choosing a Test
38. The Big Picture
Rating:
Summary: Marvelously readable and understandable
Review: An excellent supplement to math-laden statistical textbooks and courses, this book clearly and easily deals with underlying statistical theory. Covers basic essentials as well as more sophisticated application. Perfect for the public health professional that needs to understand the concepts and uses of statistical modeling for data analysis.
Rating:
Summary: Unusually readable for a stats book. A good student text.
Review: Covers the stuff most often used in biomedical sciences in a very down to earth way. Tells you what you can skip. Deals with practicalities, and doesn't dwell upon the maths, though they are there if needed
Rating:
Summary: Excellent non-mathematical overview
Review: Dr. Motulsky does an excellent job of introducing statistical concepts through examples and direct applications. Where this book is especially valuable is in keeping things simple -- without the intimidating mathematical notation -- while providing examples of where statistics can be used to measure the wrong things or present results that do not make sense in the context of what the researcher is investigating. My favorite example illustrates how a stastical analysis of a new test that identifies those susceptible to a fatal disease "shows" an increase in the average lifespan of both populations (those who suffer the disease and those who don't). The reality, of course, is no one is living longer because of the test, but rather the population sampled is different. Brilliant and concise. Although the text is targeted towards those in the bioinformatic and medical vocations, it's useful beyond that because the presentation of concepts is practical and yet without the notation.
Rating:
Summary: Excellent non-mathematical overview
Review: Dr. Motulsky does an excellent job of introducing statistical concepts through examples and direct applications. Where this book is especially valuable is in keeping things simple -- without the intimidating mathematical notation -- while providing examples of where statistics can be used to measure the wrong things or present results that do not make sense in the context of what the researcher is investigating. My favorite example illustrates how a stastical analysis of a new test that identifies those susceptible to a fatal disease "shows" an increase in the average lifespan of both populations (those who suffer the disease and those who don't). The reality, of course, is no one is living longer because of the test, but rather the population sampled is different. Brilliant and concise. Although the text is targeted towards those in the bioinformatic and medical vocations, it's useful beyond that because the presentation of concepts is practical and yet without the notation.
Rating:
Summary: book lives up to its title
Review: Dr. Motulsky is an MD who is also a Professor of Pharmacology and President of his own software company. The book's title suggests that he can make biostatistics intuitive for non-statisticians (e.g. physicians, clinicians and nurses). After reading through it he has made a believer out of me! He introduces concepts through examples and touches on most of the important statistical methods that are used in the medical literature. While the book could be used as a classroom text, it seems to me to be more suited as a reference source for medical researchers who want to understand the statistics described in research papers. Although not a statistician by training, Dr. Motulsky has a good understanding of statistical methods and principles and exhibits his wisdom and experience throughout the book. He is deliberate at keeping things simple and to the point. He points out that he intentionally uses fake examples and modifies real examples for simplification of exposition. He avoids mathematics as much as possible. the preface and the introduction are very well written and the reader should read both before reading the rest of the text. My usual concern with such books is that concepts are oversimplified and the presentation is too cook-bookish. Amazingly that is not the case here. Professor Motulsky carefully explains concepts such as confidence intervals, p-values, multiple comparison issues, Bayesian thinking and Bayesian controversy in a way that should be understandable to his intended audience. Proportions and the binomial distribution are introduced early. Advanced topics such as sequential methods, survival curves and logistic regression are tackled. These subjects are important in medical research but are often avoided in elementary books. To his credit he also does a very good job of introducing the concepts of sensitivity and specificity. Hypothesis testing is introduced at the same time which makes a lot of sense since for a particularly hypothesis test the specificity and the sensitivity are related to the type I and type II errors. It is a good way for those familiar with medical applications where specificity and sensitivity may be intuitive concepts, to become comfortable with the less familiar null and alternative hypotheses and their associated error probabilities. Professor Motulsky writes eloquently and this appears to be appreciated by the readers, judging from the other reviews that I have seen on Amazon. Having said all this you might wonder why I didn't give it 5 stars. I found a few things that could have been done better. I am not completely happy with the way probability is introduced through the binomial distribution and here the wording could be improved. He writes "Mathematicians have developed equations, known as the binomial distribution, to calculate the likelihood of observing any particular outcome when you know the proportion in the overall population." Actually the binomial distribution is a probability distribution (which he has not yet defined as he first uses the term distribution). The equation is a statement that the probability of an event (e.g. exact 7 heads in 10 coin flips) is given by equation (2.2) on page 19 with N=10 and R=7 and p=1/2 (assuming a fair coin). Another area that could be omitted or else improved is the discussion of Bayesian ideas. Bayes theorem is presented in a limited context related to the example of sensitivity and specificity. While I do think that some Bayesian ideas are well brought out the breadth of applications is missing. Some comparison of the frequentist and Bayesian approaches and philosophy are correctly described but the discussion is too brief to provide good insight. The p-value is strictly a frequentist concept. Motulsky relates it to the Bayesian idea of posterior odds for the null hypothesis to be true. While there is such a formal mathematical relationship, they are conceptually quite different. This is just like relating likelihood to posterior probability. Mathematically the likelihood and posterior probability are related through Bayes theorem as posterior = likelihood x prior but although likelihood is an acceptible frequentist concept posterior probability is not. A real understanding requires some knowledge of the sample space for a frequentist and the treatment of parameters as random quantities by Bayesians. I think this may be something that requires a little more mathematical sophistication than is intended for this readership. There are a few topics that get little or no treatment but deserve more in a biostatistics texts. These include missing data, resampling methods, hierarchical Bayesian models and longitudinal - repeated measures data. Perhaps we will see intuitive descriptions of some of these topics in the second edition.
Rating:
Summary: ACCESSIBLE
Review: I am very grateful that a professor of mine used this book for her class...I had flipped thru other stats books prior to taking her class, and the contents of those other books looked all too similar to the mathematics books that had tortured me for years.
Intuitive Biostatistics is actually enjoyable to read, and it usually teaches at a pace that is reasonable. Many of the examples were quite relevant to my area of study - Nutrition.
Wish more texts were like this one.
Rating:
Summary: A great non-technical introduction to biostatistics
Review: I often recommend this book to two different groups:
* colleagues who want to have a better understanding of the factors that drive statistical methods in medical research, without having to learn the actual statistics themselves
and
* students who are soon going to be taking biostatistics for the first time, and are anxious about whether they will be able to understand the material. For those who are a little on the math phobic side of things, this can be a great introduction to read through before formal coursework begins.
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