How to Lie with Statistics

By Darrell Huff, Irving Geis (illustrator),

Book cover of How to Lie with Statistics

Book description

From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff's lively and engaging primer clarifies the basic principles of statistics and explains how they're…

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Why read it?

3 authors picked How to Lie with Statistics as one of their favorite books. Why do they recommend it?

I am laughing out loud, even now that I am rereading this book for the umpteenth time. Fraudsters are so clever, and so is advertising. And then there is sloppy journalism with its “wow” statistics.

I like his book enormously, not least because of its witty illustrations. It is subversive, comic, and provocative, and it makes me wise to seductive, misleading practices–and it does so with a light touch.

In this classic book, the author gives numerous examples of how statistics is misused by the corporate world, government agencies, journalists, politicians, and even educational institutions.

While the book is somewhat lighthearted and somewhat lacking in rigor, it gives the reader a critical understanding of logical fallacies. I’ve recommended this book to math phobic friends who all tell me they came away with good awareness of when data is unreliable and how data can be exploited and misapplied to force certain conclusions.

How to Lie with Statistics is a book that I highly recommend to anybody just starting out in data science. While we would like to believe that data science is a science many times it’s not, it’s storytelling. This storytelling with data can quickly get us into trouble. Whether it’s shortening y-axis or presenting data in a way that makes things look better than they are.

Personally I have found this book to be invaluable especially when working with business leaders as to why I won’t do certain things to my models and presentations.

From Matthew's list on ethical artificial intelligence.

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