Name: Statistics Done Wrong
ISBN: 9781593276201

Statistics Done Wrong

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.

What Hacker News says

unhammerFeb 2022

Another good resource on how (not) to mess up statistics is https://www.statisticsdonewrong.com/

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grumblenumJul 2021

The article is about intentional error; however, I have found this to be very approachable and entertaining primer on (probably) unintentional error in research:

https://www.statisticsdonewrong.com/

Research that has been further filtered onto your favorite infotainment coffee-table-science column further suffers from the 'eigonvalue' problem. I think assuming intentional fraud is excessive for a default position, but complete trust of any document formatted with Latex because "it's science" is probably worse.

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mumblemumbleJul 2020

I would love to see a book like Alex Reinhart's Statistics Done Wrong make these lists more often.

It's not focused on data science specifically. But it's short, sweet, and just might leave you better prepared to tell whether the emperor is wearing actual clothes, or if it's just an artfully arranged assemblage of TensorFlow operators.

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caliguliminixOct 2019

Not exactly a textbook, but good to read nonetheless is, "Statistics Done Wrong"[1]. It is a short read but is filled with all the most common ways in which statistical analysis is abused with concrete examples.

[1] Statistics Done Wrong: The Woefully Complete Guide https://www.amazon.com/dp/1593276206/ref=cm_sw_r_cp_apa_i_u....

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xenocyonSep 2019

A quick 101: https://www.statisticsdonewrong.com/power.html

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cuchoiSep 2019

And that's not how to interpret the 95 and 99 thresholds.

Depending on what proportion of "Version B" ideas are truly effective, your false positive rate (what the author is concerned about) will be very far away from 5% or 1%.

At the 95 threshold (p-value of 0.05) you could have that >50% of what you identified as an "effective" version are not really effective (false positive). This is a good article to build the intuition around it: https://www.statisticsdonewrong.com/p-value.html

In the end it will also depend on how costly it is to have a false negative. The author hints that in this case is very costly.

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