eBook: Download Statistics Done Wrong Woefully Complete ePub (TXT, KINDLE, PDF) + Audio Version


  • File Size: 1486 KB
  • Print Length: 176 pages
  • Publisher: No Starch Press; 1 edition (March 1, 2015)
  • Publication Date: March 1, 2015
  • Language: English

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I want to front-load the criticism. If only an experienced statistics instructor had reviewed the manuscript. The book does better in the second half, where it discusses what We would call problems with empirical-research culture, than in its first half, which includes more textbook statistics. The author neglects to clarify the basic principles - things like " sample", " statistic", " testing distribution", " conditional probability" - and often piteuxs matters by bringing in issue Y when environment out to discuss problem X. (Appropriately, a area named " Confounding Confounders" is itself confounded: we start talking about " coarsening" data (not what I expected based on the name, by the way; a Y-for-X switch already got place), then get into something else. Let me single out the introduction to the " base-rate fallacy" an additional weak spot). A choice to be non-technical means that solutions to some problems cannot be effectively presented - although sometimes they are suggested in the end. The " woefully complete" part of the name is, I take it, tongue-in-cheek, so no quibbles there.

A few " similar" books come to mind, including (a) the drier " Common mistakes in statistics" by Phillip Good, (b) the three terrific popular books by Ben Goldacre - " Bad science", " Bad pharma" and " We think you'll find is actually a little more complicated than that" - and (c) the elegant " Understanding the new statistics" by Geoff Cumming. (I have not seen " The way to lie with statistics" by Huff and Geis). Reinhart's book is more " big-picture" than Good's, and broader than Goldacre's or Cumming's. (The latter is a perfect " single-issue" book; the former are certainly not specifically about cataloging statistics errors).

Statistical semi-literacy of empirical researchers is a serious problem, and any effort to improve the situation is to be lauded. Alex Reinhart's book - engagingly written, and nicely produced (and pretty cheaply sold) by No Starch Press - is a force for good, and something which can have a material impact., This specific book is great!

Simply ensure you are not new to statistics. In case you start with this book, you will learn nothing useful, only some tidbits here and there, mostly unconnected.

This book will tell you only about p values and power of a test. Some 90% of the contents of this book are related to what people does completely wrong with regards to g values, which is a lot, We have to say.

I had formed no idea of the current status of many science topics and also this book sadly illustrates regarding it. A person will learn that many doctors, scientists and even reviewers have no idea about what they are talking about or commenting on.

A person need to be confident with the use and understanding of many statistics before you get to read and understand this book. This is not a book for everyone, this is for sure. Neither is it a book about statistics. It is a book on the wrong use and bad implementation of p values and just how people dealing with statistics make the wrong question and have the wrong answer out of the statistics and their data sets.

If you are fluent with g values and the power of test, and you could package with hypothesis testing and all that stuff, then go read this book and you will learn something really useful.

If you are not used to statistics, this book will teach you nothing. But it is a very well written book, a nice piece of any collection. So go get it although you may are unable to deal with statistics. Then, learn statistics since it is a huge investment anyway. While you are done, read this book and find out something else., This specific is an exceptional gazette to the current bad state of statistically-based research. Reinhart is efficient in his enumeration of the major sins committed by researchers, usually unfamiliar with the nuances of the statistical concepts that are made easily available with them in stats packages. In addition, he provides many useful tricks for how to countering much of the backsliding taking place in research.

Complex topics were created easy to understand, and best of all, not a term in the text was wasted. I read this in two afternoons and am better for it., This book only has one equation so far as We remember. It doesn't clarify how to do statistics, and doesn't clarify the math showing how statistical assessments work, or maybe the proofs that they work. Instead it explains what the results suggest, and the numerous many ways statistical results can deceive. It covers evidently and humorously topics missing from almost all statistics text messaging (but not mine! ): pseudoreplication, multiple comparisons, false discovery rate, publication prejudice, Simpson's paradox, the challenge of nonreproducibility of posted findings,.... Anyone evaluating posted research, or anyone studying data, must understand these topics to understand (or do) science properly. This specific book is highly recommended., Great book that should be used as part of a course for high school graduation or college students to create numerical familiarity and healthy skepticism. Once you read this book you'll never look at the world again... especially the world we live in that is inundated with statistics and studies. From health claims to political debates there are numbers and stats being thrown about constantly. This book will give you a toolkit to begin dismantling many of these promises or at least directing you in the right direction to ask penetrating questions. Lastly - this isn't a mathematics heavy book. Anyone who can balance a checkbook should be able to make sense of what is said.

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Statistics Done Wrong Woefully Complete
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