### 3 editions of this work

For example, the term matched pairs never The Student's t distribution gets much less attention than in almost every other book; the author offers a rarely used standard-deviation change page as a way to keep things Gaussian. The author justifies the reduced topic set by calling typical "traditional" approaches flawed in the first pages of text, the Proposal. Instead, Blais tries to develop statistical inference from logic, in a way that might be called Bayesian inference.

Other books have taken this approach, more than just Donald Berry's book mentioned on page This PDF does not contain that desired textbook, however. As mentioned below under accuracy, clarity, and structure, there are too many missing elements, including the lack of an index. As I read, this PDF felt more like a augmented set of lecture notes than a textbook which stands without instructor support. It's not good enough.

**here**

## A concise introduction to statistical inference

For more on this decision, see the other comments at the end. The only non-troubling number of errors in a textbook is zero, but this book has many more than that. In the version I read from the Minnesota-hosted website, my error list includes not defining quartiles from the left page , using ICR instead of IQR page , misstating the rule as page , flipping numbers in the combination of the binomial formula page , repeating Figure C-2 as Figure C-1 page , and titling section 2. Infuriatingly, several of these mistakes are correct elsewhere in the book - Monty Hall in section 5.

I'm also annoyed that some datasets have poor source citations, such as not indicating Fisher's iris data on page and calling something "student measurements during a physics lab" on page Because there are so many gaps, including full support for computer presentation, it would be easy to update completed sections as needed, such as when Python becomes less popular. Quality of the prose is fine, but many jargon terms are not well defined.

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Students learning a subject need clear definitions, but they don't appear. In my notes, I see exclusive page 36 , conditioning page 40 , complement used on page 40 but never appears in the text , posterior page 54 , correlation page 55 , uniform distribution page , and Greek letters for which the reference to a help table appears on page , but Greek letters have appeared earlier. I appreciate that the author is trying to avoid calculus with "area under the curve" on page , but there's not enough written for a non-calculus student to understand how these probabilities are calculated.

To really understand posterior computation, a magical computer and a few graphs aren't good enough. Internal consistency to Bayesian inference is quite strong; many of the examples repeat the steps of Bayes' Recipe.

This is not a concern. The book needs to be read in linear order, like most statistics books, but that's not necessarily a negative thing. Blais is trying to take the reader through a structured development of Bayesian inference, which has a single path.

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There are a few digressions, such as fallacies about probability reasoning, but the book generally maintains a single path from chapters 1 to at least 7. Most sections are less than 10 pages and don't involve lots of self-references. Although I rated reorganization possibility as low, due to the near-impossibility of realigning the argument, I consider it harsh to penalize the book for this.

There isn't enough structure for a textbook; this feels more like a set of augmented lecture notes that a book for guided study. I mentioned poor definitions under "Clarity", so let me add other topics here.

## Statistical Inference : A Short Course - ScholarVox International

The word prior first appears on page 48, but receives no clear definition until a side-note on page The word posterior first appears on page Despite this, the fundamental equation is never written with all three words in the correct places until page That's way, way too late. The three key terms should have been defined around page 50 and drilled throughout all the sections. The computer exercises also have terrible structure.

The first section with computer exercises, section 2. The reader has no idea about the language, package, or purpose of these weird words in boxes. The explanation about Python appears as Appendix A, after all the exercises.

It would not have taken much to explain Python and the purpose of the computer exercises in Chapter 1 or 2, but it didn't happen. A classroom instructor could explain this in class, but the Open Resource Project doesn't provide an instructor with every book. Like the other things mentioned, the structure around computing is insufficient. Nonparametric methods are considered as a possible alternative in case the requirements of the t-test are not met.

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