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You are here: Home > Suggested readings > Best - Stat-Spotting: A Field Guide to Identifying Dubious Data - ISBN 9780520257467 - 5/5

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Published on 2018-04-23 | Updated on 2018-04-25 20:49:00 | words: 443


Best, Joel
Stat-Spotting: A Field Guide to Identifying Dubious Data
BookID 82779292
ISBN 0520257464
(see LibraryThing.com card)
Description (from Amazon)"Are four million women really battered to death by their husbands or boyfriends each year? Does a young person commit suicide every thirteen minutes in the United States? Is methamphetamine our number one drug problem today? Alarming statistics bombard our daily lives, appearing in the news, on the Web, seemingly everywhere. But all too often, even the most respected publications present numbers that are miscalculated, misinterpreted, hyped, or simply misleading. Following on the heels of his highly acclaimed Damned Lies and Statistics and More Damned Lies and Statistics, Joel Best now offers this practical field guide to help everyone identify questionable statistics. Entertaining, informative, and concise, Stat-Spotting is essential reading for people who want to be more savvy and critical consumers of news and information.

Stat-Spotting features:

* Pertinent examples from today's news, including the number of deaths reported in Iraq, the threat of secondhand smoke, the increase in the number of overweight Americans, and many more

* A commonsense approach that doesn't require advanced math or statistics "
My review: 5/5"We live surrounded by numbers and statistics.

But we are wildly underequipped with the critical skills needed to understand them.

So, both us as individual citizens, and corporations or politicians are more often than not fooled by our own expectations, and... some snake oil peddlers.

This book is useful as an entry point to avoid being fooled (too often), moreover when numbers are couple with charts or that Internet demon, Infographic (where our brain is more easily fooled by catchy pictures that generate a seemingly logical path from false assumptions)

If anything, this could be used to avoid too many journalists and economists playing the ""Freaknomics"" game by selecting data and then trying to find something that is coherent with their expectactions (that, in some cases, is what they expect that could attract an audience)

Personally: whenever working in business inteligence or management reporting (or being dragged into discussions on big data, machine learning, etc), since reading it, I like to eventually use it as a reference to quickly put off the table some misleading assumptions"
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