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THIS IS FOR THE 2nd EDITION. THE 3rd EDITION IS NOW AVAILABLE.OverviewIntuitive Biostatistics is both an introduction and review of statistics. Compared to other books, it has:
- Breadth rather than depth. It is a guidebook, not a cookbook.
- Words rather than math. It has few equations.
- Explanations rather than recipes. This book presents few details of statistical methods and only a few tables required to complete the calculations.
- Medical (and other) professionals who want to understand �the statistical portions of journals they read. These readers don't need to analyze any data, but need to understand analyses published by others.
- Undergraduate and graduate students, post-docs and researchers who will analyze data. This book explains general principles of data analysis, but it won't teach you how to do statistical calculations or how to use any particular statistical program.�
- Scientists who consult with statisticians. Statistics often seems like a foreign language, and this text can serve as a phrase book to bridge the gap �between scientists and statisticians.
- Chapter 1 explains how our intuitions can lead us astray in issues of probability and statistics.
- Chapter 11 (and later examples) highlight the fact that lognormal distributions are common.
- Chapter 21 explains the idea of testing for equivalence vs. testing for differences.�
- Chapters 22, 23, and 40 discuss the pervasive problem of multiple comparisons.�
- Chapters 24 and 25 discuss testing for normality and for outliers.
- Chapter 35 shows how to think about statistical hypothesis testing as comparing the fits of alternative models.
- Chapters 37 and 38 give expanded coverage of the usefulness--and traps--of multiple, logistic, and proportional hazards regression.
- Chapter 43 briefly mentions adaptive study designs where sample size is not chosen in advance.
- Chapter 46 (inspired by, and written with, Bill Greco) reviews many topics in this book and more general issues of how to approach data analysis.
- Sales Rank: #362284 in Books
- Published on: 2010-01-20
- Original language: English
- Number of items: 1
- Dimensions: 6.20" h x 1.00" w x 9.20" l, 1.40 pounds
- Binding: Paperback
- 508 pages
Review
I am entranced by the book. �Statistics is often difficult for many scientists to fully appreciate. Your writing style and explanation makes the concepts accessible.� ----Tim Bushnell, Director of Flow Cytometry, Univ. Rochester Med. Center (added by author)
"The second edition of Intuitive Biostatistics is a substantial improvement. I am particularly impressed by the chapters on multiple comparisons. This is increasingly important for such molecular trickery as gene expression chips, which produce a very large number of possible comparisons. Individual comparisons and even a Bonferroni correction are often inadequate. Motulsky deals with a newer method, false discovery rate (FDR), in a clear, understandable way. I'll be recommending the new edition with even greater enthusiasm."--James F. Crow, University of Wisconsin
"This splendid book meets a major need in public health, medicine, and biomedical research training--a user-friendly biostatistics text for non-mathematicians that clearly explains how to make sense of statistical results and how to avoid being confused by statistical nonsense. You may enjoy statistics for the first time!"--Gilbert S. Omenn, Professor of Medicine, Genetics, Public Health, and Computational Medicine & Bioinformatics, University of Michigan
From the Author
View the web page for this book, including errata, at intuitivebiostatistics.com
CONTENTS FOR 2nd EDITION (3rd NOW AVAILABLE)Part A: Introducing Statistics�1. Statistics and Probability Are Not Intuitive 32. Why Statistics Can Be Hard to Learn 143. From Sample to Population 17Part B: Confidence Intervals�4. Confidence Interval of a Proportion 255. Confidence Interval of Survival Data 386. Confidence Interval of Counted Data 47Part C: Continuous Variables�7. Graphing Continuous Data 578. Types of Variables 679. Quantifying Scatter 7110. The Gaussian Distribution 7811. The Lognormal Distribution and Geometric Mean 8312. Confidence Interval of a Mean 8713. The Theory of Confidence Intervals 9614. Error Bars 103PART D: P Values and Significance�15. Introducing P Values 11116. Statistical Significance and Hypothesis Testing 12217. Relationship Between Confidence Intervals and Statistical Significance 13018. Interpreting a Result That Is Statistically Significant 13419. Interpreting a Result That Is Not Statistically Significant 14120. Statistical Power 14621. Testing for Equivalence or Noninferiority 150PART E: Challenges in Statistics�22. Multiple Comparisons Concepts 15923. Multiple Comparison Traps 16824. Gaussian or Not? 17525. Outliers 181PART F: Statistical Tests�26. Comparing Observed and Expected Distributions 19127. Comparing Proportions: Prospective and Experimental Studies 19628. Comparing Proportions: Case-Control Studies 20329. Comparing Survival Curves 21030. Comparing Two Means: Unpaired t Test 21931. Comparing Two Paired Groups 23132. Correlation 243PART G: Fitting Models to Data�33. Simple Linear Regression 25534. Introducing Models 27035. Comparing Models 27636. Nonlinear Regression 28537. Multiple, Logistic, and Proportional Hazards Regression 29638. Multiple Regression Traps 315PART H The Rest of Statistics 32139. Analysis of Variance 32340. Multiple Comparison Tests After ANOVA 33141. Nonparametric Methods 34442. Sensitivity and Specificity and Receiver-Operator Characteristic Curves 35443. Sample Size 363PART I Putting It All Together 37544. Statistical Advice �37745. Choosing a Statistical Test �38746. Capstone Example 39047. Review Problems 40648. Answers to Review Problems 418Appendices�A. Statistics With GraphPad 451B. Statistics With Excel 456C. Statistics With R 458D. Values of the t Distribution Needed to Compute CIs 460E. A Review of Logarithms 462��
From the Inside Flap
Excerpt from "Statistics means being uncertain" (chapter 3, page 19)The whole idea of statistics is to make general conclusions from limited amounts of data. All that statistical calculations can do is quantify probabilities, so every conclusion must include words like "probably," "most likely," or "almost certainly." Be wary if you ever encounter statistical conclusions that seem 100% definitive. The analysis, or your understanding of it, is probably wrong. Be especially wary of the conclusion that a result is statistically significant, because that phrase is often misunderstood.
Excerpt from "Q and A about confidence intervals" (chapter 4, pages 35-36)Q. What's the difference between a 95% CI and a 99% CI?A. To be more certain that an interval contains the true population value, you must generate a wider interval. A 99% CI is wider than a 95%CI. See Figure 4.2.
Q. Is it possible to generate a 100% CI?A. A 100% CI would have to include every possible value, so it would extend from 0.0 to 100.0%. That is always the same, regardless of the data, so it isn't at all useful.
Q. How do CIs change if you increase the sample size?A. The width of the CI is approximately proportional to the reciprocal of the square root of the sample size. So, if you increase the sample size by a factor of 4, you can expect to cut the length of the CI in half. Figure 4.3 illustrates how the CI gets narrower if the sample size gets larger.
Q. Why isn't the CI symmetrical around the observed proportion?A. Because a proportion cannot go below 0.0 or above 1.0, the CI will be lopsided when the sample proportion is far from 0.50 or the sample size is small. See Figure 4.4.
Excerpt from "A misconception about P values" (chapter 18, page 136)Many scientists and students misunderstand the definition of statistical significance (and P values).Table 18.1 shows the results of many hypothetical statistical analyses, each analyzed to reach a decision to reject or not reject the null hypothesis. The top row tabulates results for experiments where the null hypothesis is really true.
The second row tabulates experiments where the null hypothesis is not true. This kind of table is only useful to understand statistical theory. When you analyze data, you don't know whether the null hypothesis is true, so you could never create this table from an actual series of experiments.
�Table 18.2 reviews the definitions of Type I and Type II errors.The significance level (usually set to 5%) is defined to equal the ratio A/(A + B). The significance level is the answer to these two equivalent questions:
- �If the null hypothesis is true, what is the probability of incorrectly rejecting that null hypothesis?
- Of all experiments you could conduct when the null hypothesis is true, in what fraction will you reach a conclusion that the results are statistically significant?
- If a result is statistically significant, what is the probability that the null hypothesis is really true?
- Of all experiments that reach a statistically significant conclusion, in what fraction is the null hypothesis true?
- How long did he spend looking? If he looked for �a long time, he is more likely to have found the tool. This is analogous to sample size. An experiment with a large sample size has high power to find an effect.
- How big is the tool? It is easier to find a snow shovel than the tiny screw driver used to fix eyeglasses. This is analogous to the size of the effect you are looking for. An experiment has more power to find a big effect than a small one.
- How messy is the basement? If the basement is a real mess, he was less likely to find the tool than if it is carefully organized. This is analogous to experimental scatter. An experiment has more power when the data are very tight (little variation).
If the child spent a long time looking for a large tool in an organized basement, there is a high chance that he would have found the tool if it were there. So you can be quite confident of his conclusion that the tool isn't there. Similarly, an experiment has high power when you have a large sample size, are looking for a large effect, and have data with little scatter (small standard deviation). In this situation, there is a high chance that you would have obtained a statistically significant effect if the effect existed.
If the child spent a short time looking for a small tool in a messy basement, his conclusion that "the tool isn't there" doesn't really mean very much. Even if the tool were there, he probably would have not found it. Similarly, an experiment has little power when you use a small sample size, are looking for a small effect, and the data have lots of scatter. In this situation, there is a high chance of obtaining a conclusion of "statistically significant even if the effect exists.
Most helpful customer reviews
6 of 6 people found the following review helpful.
Excellent book for understanding statistics
By Tanya Ane
I studied statistics nearly all my life, and even then, this book was an enlightenment to me. I would recommend it not only to biostatistocians, but also for other fields where statistics is heavily used.
I bought this book as a recommendation from my supervisor, who owns a previous edition. But after I started reading it, and tell him the concepts, and why our data looked like it did, and why we did not get significant results or why we did get it, and whats a true meaning of t-statistics, and the theory behind confidence intervals, we decided to compare the versions, and we realized that first edition was way more formal and mathematical. So now he ordered this new addition too.
There are nearly no formulas in the book, it is very easy to read from head to tail. It's almost like reading a novel!
I am already half way through and I am very looking forward for the next chapter, which in fact never happened to me when reading a scientific material. Also, Q&A section is very nice, where you can check your understanding of concepts, or just see the correct answers to tricky questions.
5 of 5 people found the following review helpful.
Intuitive Biostatistics: A Life Saver!
By GarageBoy
I'm a retired cancer researcher (molecular biology) turned biomedical writer/editor. My use of statistics (and increasing ignorance thereof) was limited, because, in general, if an experiment didn't yield at least a 10x difference, we threw it out. Now I can't do my job without knowing biostatistics. Moltulsky's book has been enormously helpful in teaching me the basics of biostatistics and enabling me to evaluate manuscripts for correct use of statistical terms and methods. Previously I used (and still do) Motulsky's manual for his GraphPad software, the AMA Manual online, and, of course, the internet. The only problem with the book is that a digital version isn't available. However, as the author pointed out to me, you can search the book to some extent on its Amazon.com product page. Finally, the author's immediately available via email. This is a terrific resource and value!
4 of 4 people found the following review helpful.
Outstanding
By Bitz
This is a wonderful book that clearly and creatively explains some difficult epidemiologic and biostatistical concepts. As a practicing epidemiologist I find this book an invaluable reference. A must have for any graduate student in public health.
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