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39 of 41 people found this helpful

ByS. Bartellon February 7, 2011

This is the second year that I've taught introductory public health statistics out of Rosner's book.

Pros:

* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.

* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.

* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!

* Great section on epidemiologic methods in the final chapters, including logistic regression.

* Good coverage of exact methods and a short chapter on classical non-parametrics.

* Includes some discussion of missing data analysis.

* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.

Cons:

* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.

* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.

* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.

* More emphasis than I'd like on normal approximations for binomial and poisson distributions and corresponding one-sample tests. Again, this seems to cause needless confusion considering that it is much simpler for the students to obtain exact results.

* Some of the example homework problems are not worded very clearly, so be careful about selection. This also leads to a few surprising interpretations/answers in the online solutions.

* Minor quibble: two-sample binomial tests are covered in two different sections of the book, depending on which version of the effect estimate is used (p2-p1, p2/p1, odds ratio, etc.). I wish they were all presented together.

Pros:

* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.

* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.

* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!

* Great section on epidemiologic methods in the final chapters, including logistic regression.

* Good coverage of exact methods and a short chapter on classical non-parametrics.

* Includes some discussion of missing data analysis.

* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.

Cons:

* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.

* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.

* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.

* More emphasis than I'd like on normal approximations for binomial and poisson distributions and corresponding one-sample tests. Again, this seems to cause needless confusion considering that it is much simpler for the students to obtain exact results.

* Some of the example homework problems are not worded very clearly, so be careful about selection. This also leads to a few surprising interpretations/answers in the online solutions.

* Minor quibble: two-sample binomial tests are covered in two different sections of the book, depending on which version of the effect estimate is used (p2-p1, p2/p1, odds ratio, etc.). I wish they were all presented together.

21 of 23 people found this helpful

Bylsgranton September 2, 2012

The Kindle edition is essentially useless. The equations don't show up and none of the tables show up correctly. I have homework in this class for which I have to use this book and I am EXTREMELY disappointed that I have to inconveniently commute back and forth to the library to photocopy multiple pages from the text--spending more money, which is what I was trying to avoid by having the Kindle edition. You should take this off as something you can purchase. I wouldn't even want it if it was free!

ByS. Bartellon February 7, 2011

This is the second year that I've taught introductory public health statistics out of Rosner's book.

Pros:

* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.

* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.

* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!

* Great section on epidemiologic methods in the final chapters, including logistic regression.

* Good coverage of exact methods and a short chapter on classical non-parametrics.

* Includes some discussion of missing data analysis.

* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.

Cons:

* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.

* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.

* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.

* More emphasis than I'd like on normal approximations for binomial and poisson distributions and corresponding one-sample tests. Again, this seems to cause needless confusion considering that it is much simpler for the students to obtain exact results.

* Some of the example homework problems are not worded very clearly, so be careful about selection. This also leads to a few surprising interpretations/answers in the online solutions.

* Minor quibble: two-sample binomial tests are covered in two different sections of the book, depending on which version of the effect estimate is used (p2-p1, p2/p1, odds ratio, etc.). I wish they were all presented together.

Pros:

* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.

* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.

* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!

* Great section on epidemiologic methods in the final chapters, including logistic regression.

* Good coverage of exact methods and a short chapter on classical non-parametrics.

* Includes some discussion of missing data analysis.

* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.

Cons:

* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.

* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.

* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.

* More emphasis than I'd like on normal approximations for binomial and poisson distributions and corresponding one-sample tests. Again, this seems to cause needless confusion considering that it is much simpler for the students to obtain exact results.

* Some of the example homework problems are not worded very clearly, so be careful about selection. This also leads to a few surprising interpretations/answers in the online solutions.

* Minor quibble: two-sample binomial tests are covered in two different sections of the book, depending on which version of the effect estimate is used (p2-p1, p2/p1, odds ratio, etc.). I wish they were all presented together.

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Bylsgranton September 2, 2012

The Kindle edition is essentially useless. The equations don't show up and none of the tables show up correctly. I have homework in this class for which I have to use this book and I am EXTREMELY disappointed that I have to inconveniently commute back and forth to the library to photocopy multiple pages from the text--spending more money, which is what I was trying to avoid by having the Kindle edition. You should take this off as something you can purchase. I wouldn't even want it if it was free!

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ByWhitakeron June 12, 2012

I grew frustrated with this book and sought out other ones, but in the end I returned to this book, because it is more comprehensive and contains more examples. These are easily its biggest strengths. But there are two huge, immensely frustrating downsides:

1. The language is downright incomprehensible. It is overly technical; it is entirely math-speak; he will use symbols and equations over words any day, including in a regular paragraph. When he wants to explain a concept, he often does so by deriving it mathematically through a series of half a dozen equations instead of just explaining in plain english what the statistical test does. There are times when I'm exasperated from trying to read two dozen mathematical equations trying to understand his point, only to look in another textbook and realize it was something I already knew and to laugh -- he had literally made something I already knew completely unrecognizable.

2. The author's habit of referring to previous examples and equations by their numbers only, for example "Equation 6.14", without reminding you what they were about. Sometimes he refers to an example or equation all the way back in the previous chapter like this, like you were supposed to just have memorized "Equation 6.14". It requires you to flip back and forth a whole lot.

For example in the first two sentences of section 7.8 ... well into chapter 7 ... he writes, "One limitation of the methods of interval estimation in Section 6.5 is that it is difficult to make direct statements such as Pr(c1 < ' < c2) = 1 ' '. Instead, we have made statements such as Equation 6.7." Of course, he doesn't remind you what Equation 6.7 is. You're just supposed to remember equation 6.7 and section 6.5, which were in the previous chapter. He does this with examples, too, building on "example 6.1" without reminding you in the least what it was about or how far you've gotten on it.

But I returned to this book in the end and dealt with it because, once again, it is more comprehensive and provides more examples. But I tend to have a couple of other books on hand to make the job easier, e.g. one of those simplified statistics books and an alternate textbook. They rarely go into as much detail as Rosner but I pretty much have to read the relevant sections in those books first to even get the general idea of what Rosner is trying to explain.

1. The language is downright incomprehensible. It is overly technical; it is entirely math-speak; he will use symbols and equations over words any day, including in a regular paragraph. When he wants to explain a concept, he often does so by deriving it mathematically through a series of half a dozen equations instead of just explaining in plain english what the statistical test does. There are times when I'm exasperated from trying to read two dozen mathematical equations trying to understand his point, only to look in another textbook and realize it was something I already knew and to laugh -- he had literally made something I already knew completely unrecognizable.

2. The author's habit of referring to previous examples and equations by their numbers only, for example "Equation 6.14", without reminding you what they were about. Sometimes he refers to an example or equation all the way back in the previous chapter like this, like you were supposed to just have memorized "Equation 6.14". It requires you to flip back and forth a whole lot.

For example in the first two sentences of section 7.8 ... well into chapter 7 ... he writes, "One limitation of the methods of interval estimation in Section 6.5 is that it is difficult to make direct statements such as Pr(c1 < ' < c2) = 1 ' '. Instead, we have made statements such as Equation 6.7." Of course, he doesn't remind you what Equation 6.7 is. You're just supposed to remember equation 6.7 and section 6.5, which were in the previous chapter. He does this with examples, too, building on "example 6.1" without reminding you in the least what it was about or how far you've gotten on it.

But I returned to this book in the end and dealt with it because, once again, it is more comprehensive and provides more examples. But I tend to have a couple of other books on hand to make the job easier, e.g. one of those simplified statistics books and an alternate textbook. They rarely go into as much detail as Rosner but I pretty much have to read the relevant sections in those books first to even get the general idea of what Rosner is trying to explain.

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By50 something physician, teacher, scientiston December 10, 2013

This is the 7th Edition, yet the Harvard staff appears not have enough competent staff nor funds to edit and proofread texts, nor correct medical and general biological information errors they produce. I studied at Harvard for a specific public health project, and found that there is a distinct information transmission disconnect among many Harvard professors. So, if they don't have the skill or patience to explain a concept or answer a question, they simply skip over that section, provide a skeletal description, or speed up if they are speaking. I will step far out on a limb here and risk being shot at, but I think my suggestion is solid: Harvard folks should not write basic or summary texts. If they do attempt such an endeavor, they should have a co-author who is a mere mortal, someone who cares enough to truly explain concepts to the masses of lower forms of life who don't work at Harvard and were not "trained" to ignore the masses by attending Harvard.

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ByAmazon Customeron June 20, 2013

I had to get this book for an introductory biostatistics class and I had previously had basic statistics classes. For a book that is supposed to be introductory, "fundamentals", it is unnecessarily complex. Further, it does not do an adequate job of explaining topics and defining terms and symbols (it uses one symbol in equations repeatedly that I never could find defined in the book). If you are looking for a book to educate yourself, look elsewhere. If you are a student who needs this book for a class and you have not had a statistics class before, I would recommend you also buy a supplementary text. I like the Cartoon Guide to Statistics by Gonick and Smith, and I heard from another student that Statistics for Dummies was helpful. If you are a professor, I urge you to look for another text book, or if you think this is the best one, remember, texts should supplement the class, so teaching straight from this book will be a detriment to your students.

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Byscapegoaton October 5, 2011

Here's the thing, I needed to buy the 7th edition of this book for a class. Turns out Amazon has a paperback and it's a lot less money. That is great except when you click on the zoom to see the front cover for the paperback, you do not see the paperback. Which is not useful because the paperback is the international edition. Now I have a book that does not match up with my assignments. Awesome, Amazon. Thanks a ton. I think it's folly to sell these two books side by side as if they are completely interchangable.

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ByDavid C Tabanoon October 2, 2013

The text is good, but the layout of the information in chapters are a bit cumbersome. The examples and solutions aren't clear.

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ByHillaryon May 12, 2013

I took Dr.Rosner's class and the book is useless. It is convoluted, hard to understand and follow.Constantly flipping from page to prior pages to see charts, etc. For the cost it was a waste of money. Save your money and find a different book. For reasons of potential slander I won't even describe how bad the class was but it was reflected in how bad the book was.

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ByWu Danon December 22, 2013

A generally favorable review (of the 6th edition) can be found at The American Statistician, Vol 61, No 2, 2007, page 183. (DOI: 10.2307/27643876)... A critical review of the text (7th edition) can be found at Journal of Biopharmaceutical Statistics Vol 21, Issue 5, 2011, pages 1046-1048. (DOI: 10.1080/10543406.2011.592364).

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ByRCon April 26, 2014

I bought this for an introductory biostats course. Considering how expensive this book is, it is sooo not worth it. The textbook is poorly organized. I find myself getting frustrated with how the content is structured. The example questions were placed randomly and I recall what a pain it was finding the answers and trying to learn from it. I sold the book once I was done with finals.

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