Statistics Done Wrong
The Woefully Complete Guide
Publisher: No Starch Press
Final Release Date: March 2015
Pages: 176

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.

You'll find advice on:

  • Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan
  • How to think about p values, significance, insignificance, confidence intervals, and regression
  • Choosing the right sample size and avoiding false positives
  • Reporting your analysis and publishing your data and source code
  • Procedures to follow, precautions to take, and analytical software that can help

Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know.

The first step toward statistics done right is Statistics Done Wrong.

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oreillyStatistics Done Wrong

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(2 of 2 customers found this review helpful)


Most Common Abuses of Statistics


from Shaker Hts, OH

Verified Reviewer


  • Helpful examples
  • Well-written


    Best Uses

    • Intermediate

    Comments about oreilly Statistics Done Wrong:

    Six Sigma, data driven decision-making and other similar business practices rely on data collection and interpretation. Statistics is one of the most used tools to help with data interpretation. Of course inappropriate use of analytical tools leads to questionable conclusions. This book explores some of the most common areas where statistics are not appropriately applied. It uses publications in the medical field to illustrate where these errors occurred, and given the types of journals, just how widespread statistical abuse (albeit unintentional) can be.

    The first nine chapters explore some of the most common areas where the results of statistical tests are misunderstood and hence the conclusions are suspect. The last three offer suggestions on making raw data for readily available for others to review and critique. It is not this book's objective to go through the mathematical derivations of statistical theory and show where its assumptions are violated, but rather to state the assumptions and demonstrate how they are violated in the published literature. While the title claims it is a complete guide, readers should be aware that it covers the most common statistical errors, not all of them.

    The book does not have many formulas, the text is easy to read and the narrative is sometimes light-hearted (at least as light-hearted as narratives on statistics can get). While it is easy to read, readers without a statistical background, and some of the statistical jargon may have a difficult time following the text. Readers familiar with p-values, sample size and power, replication, types of bias, regression, and ANOVA should not have difficulty following the text. A light-read – and good book – for those with a background in statistics. I received this book as a review copy.

    (4 of 4 customers found this review helpful)


    Read this before you know "everything" ! It's an eye opener.

    By John B

    from Buffalo, NY

    About Me Developer

    Verified Buyer


    • Concise
    • Easy to understand


      Best Uses

      • Expert
      • Intermediate
      • Novice
      • Student

      Comments about oreilly Statistics Done Wrong:

      This is a one-of-a-kind book. I am not a statistics nerd, but I found that it challenged what I learned in school decades ago. From time to time, everyone needs a wake-up call or reality-check, even in a well established, staid field like stats.

      The book's value is that it pokes holes in conventional knowledge. It is definitely NOT a study guide for basic statistics, but it will help you avoid unintentional misuse of the statistical techniques that you already practice. It's pleasantly compact and to the point. I found the author's style to be both engaging and transparent. Perhaps I am in the minority, but I think every scientist, engineer and researcher should digest this book before they consider producing a statistical analysis for their next project!

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