Author(s): Seth Stephens-DavidowitzDownload
Publish: Published May 9th 2017 by Dey Street Books
ISBN: ISBN 0062390856 (ISBN13: 9780062390851)
An Economist Best Book of the Year
A PBS NewsHour Book of the Year
An Entrepeneur Top Business Book
An Amazon Best Book of the Year in Business and Leadership
New York Times Bestseller
Foreword by Steven Pinker, author of The Better Angels of our Nature
Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.
By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.
Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?
Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.
Some Reviews: 18,709 ratings 1,836 reviews in Goodreads.com
However, the second half of the book covers a bit more of human behaviour, data misrepresentations and the extent to which big data can be applied. I found that to be unique and informative enough to do some research on my own.
Although Big Data is no longer considered to be the new kid on the block, it is still very important (Google and Facebook are definitely making a hefty profit out of it), but the sooner fields like psychology, sociology, anthropology are going to embrace it, the better for us as human beings.
Davidowitz recognises that all results are open to subjective interpretation (correlation vs causation), but I’m not convinced on his suggestion of utilising the results regardless of understanding causality. For example, he discusses the case study of Jeff Seder who could predict the success of a race horse basis the size of its internal organs. Davidowitz claims that Seder is in the prediction business, not explanation business and therefore does not need to worry about why the race horse model worked. But without understanding the underlying relationships, aren’t we setting ourselves up for the errors of dimensionality? I mean, if you torture data enough, it’ll give you a trend/relationship. But would this hold true for all data? It seemed he was contradicting himself at this point.
Overall, it’s a very quick read and great for anyone wanting to understand the power of Big Data.
This book is an interesting look at data and how companies use that data and ways these companies and public entities could be using it.
It introduces lots of interesting concepts I hadn’t previously been aware of such as finding a twitter doppelganger, how companies can determine if someone will pay back a loan based on the words they use, and proof of an implicit bias favoring men and boys. There’s also some unsettling concepts like how someone’s sexual proclivities are determined for life around middle school and how many people are searching for incest porn. The fascinating concept behind this horrifying discovery is that despite what people may say socially at the end of the day unidentified data tells the real story.
This was a fairly easy to read book that introduces a variety of thoughts about data in a way that isn’t overwhelming. I also feel like I learned things including how to track google search trends and other ways data can be used.
While this certainly isn’t a comprehensive look at modern data tracking and the many ways data is used on a daily basis, it’s a good read that presents some big ideas.
For a data professional like me, this book sparks creative ideas on what data to collect (power of Big Data: #1 offering up new types of data and #2 providing honest data), how to zoom in (#3 allowing us to zoom in on small subsets of people), how to design clever experiments (#4 allowing us to do many causal experiments), etc. My favorite examples include how Google searches reveal the truth about racism — and data-driven recommendations on how Obama’s speech might have been more effective in alleviating Muslin hate; how different cultures around the world impact pregnant women’s concerns; how super bowl provided perfect natural experiment to quantify the effectiveness of advertising…
I have recommended the book to my colleagues and many of the stories have been hovering around in our chats.
Despite this shortcoming, the book is still worth reading, especially for anyone interested in learning more about big data.
I was fascinated to learn that the map with the closest correlation to explain why Trump won was one that showed a match for areas where people goggle “N-word jokes.” This would explain why Trump is hesitant to criticize white supremicists.
Big data tends to reveal what people do as opposed to surveys which only reveal what people want the surveyors to hear. Findings are not for the faint-hearted.