This book contains a lot of fundamental mistakes. Iâ€™m not talking about typos that some people complain about. I mean some fundamental mistakes, where the corrected information is written in fairly basic probability and statistics books. Here are some of them:1. p.31: The authors claim that the Lorentzian (Cauchy) distribution has the mean mu. In fact, the mean is not defined. The parameter mu is the median, but not the mean (although the distribution is symmetric). If the importance of this fact is not clear to you, here is an example. If Lorentzian (Cauchy) distribution had the mean, the law of large numbers would apply to it, but in fact it does not. Google Cauchy distribution for more info.2. p. 66 (both figures): The authors claim that the distribution of the number of points in each bin is Poisson. In fact, it is binomial. Although binomial converges to Poisson, the approximation is reasonable for really small p (think of variance of binomial, which is (1-p) times variance of Poisson (lambda=n*p), so with p=0.1 we still get 10% difference).3. p. 67 formula (4.32): The authors divide by variance, which may seem intuitive, but in fact you are supposed to divide by the Expected Count. Since they incorrectly assume Poisson, they end up with the correct denominator n*p (lucky for them). If they correctly used the binomial, they would get n*p*(1-p), which is incorrect. If you correctly use binomial and the Expected Count, you get the correct denominator n*p.4. p. 67 formula (4.33): The first part of that equation is correct, but then the authors feel the need to replace n*p with the observed count (h(x)), assuming that n*p is approximately h(x). You never want to do this for two reasons:  a. The whole purpose of the chi-squared test is to check if n*p is close to h(x). If n*p is approximately h(x), you might as well write zero in the numerator.  b. The chi-squared approximation will not work well.5. p. 66 (both figures): The bins for the chi-squared statistic should have equal p, not equal width (like it is done for histograms). With equal widths the chi-squared approximation does not work well for small samples.I could continue, but I think I will stop here. This looks pretty embarrassing. The book seems to be based on what authors thought to be correct, rather than checking facts with other statistical books. I know it is supposed to be a practical book, but the facts should be correct, and using an incorrect formula (for example, for chi-squared) is not good for practitioners.The explanations are also poor as pointed out by other reviewers. One example is Section 3.3 on Error Propagation, where all formulas are shown as exact, although in fact, some of them are approximate and some are exact. In other context, the authors are careful to write that, for example, square root of 723 is approximately 26.9. The approximation in Error Propagation formulas can be much cruder of course, but this is not discussed, other than a general discussion of approximations in Section 3.2.I was sad to see that this book apparently is a classic, and Iâ€™m guessing it has been used by generations of scientists. The only good news is that students typically do not remember all that was written in their textbook.The book is quite fascinating for me as an example of how people knowing so much about probability and statistics can be so deeply confused by it. That is correct; authors know a lot about the topic. I guess enough to be dangerous, as the saying goes.