{"id":72989,"date":"2022-07-18T13:58:00","date_gmt":"2022-07-18T17:58:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=72989"},"modified":"2022-07-27T13:54:36","modified_gmt":"2022-07-27T17:54:36","slug":"everybody-lies-book","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/everybody-lies-book\/","title":{"rendered":"Everybody Lies: Book Overview and Takeaways"},"content":{"rendered":"\n

What is the book Everybody Lies<\/em> about? What should you take away from the book?<\/p>\n\n\n\n

In Everybody Lies<\/em>, Seth Stephens-Davidowitz argues that people willingly confess all of their secrets in their Google searches and other web activity.\u00a0This information can be found through big data and can be used for the greater good.<\/p>\n\n\n\n

Read below for a brief overview of the book Everybody Lies<\/em> by Seth Stephens-Davidowitz.<\/p>\n\n\n\n\n\n\n\n

Everybody Lies<\/em> Book Overview<\/strong><\/h2>\n\n\n\n

In his book Everybody Lies<\/em><\/a>, Seth Stephens-Davidowitz <\/em>explains <\/em>big data\u2019s potential to revolutionize social science research. The book\u2019s central premise is that people reveal more about themselves when making web searches than they would ever reveal in public or in a traditional survey<\/strong>. Stephens-Davidowitz argues that by harnessing data from search results and similar sources, scientists have access to all new insights into issues like sexuality, racism, and health. He suggests that these insights can inform better social policies, improve institutions like education and health care, promote social equity, and bring hidden injustice to light. <\/p>\n\n\n\n

Stephens-Davidowitz has a Ph.D. in economics and has worked as a data scientist at Google and as a contributor to TheNew York Times<\/em>. Everybody Lies <\/em>draws on his research using Google search results as well as data from PornHub, Wikipedia, and more. Though the book contains many surprising and interesting findings from this research, its real purpose is to explain the benefits\u2014and drawbacks\u2014of big data research. Stephens-Davidowitz says he hopes to inspire readers to enter the field of data science, much as Steven Levitt and Stephen J. Dubner\u2019s Freakonomics<\/em><\/a> <\/em>inspired him to do the same.<\/p>\n\n\n\n

Why Data?<\/strong><\/h2>\n\n\n\n

Before we get into the specifics of how to use big data well, let\u2019s look at the bigger picture<\/a> of what big data is and why Stephens-Davidowitz says we should care about it. Though data science might seem arcane, Stephens-Davidowitz argues that it\u2019s really an extension of our natural intuition and of the kinds of studies social scientists have always been interested in.<\/p>\n\n\n\n

What Is \u201cBig Data\u201d?<\/strong><\/h3>\n\n\n\n

Stephens-Davidowitz explicitly refuses to define \u201cbig data,\u201d arguing that the only way to do so is by assigning an arbitrary numerical cutoff\u2014in other words, by deciding that if you have at least X data points, you have big data. While it\u2019s fair to say that big data is a somewhat fluid concept, other data experts accept that there are some key characteristics that we can use to pin down \u201cbig data.\u201d Chief among these characteristics are the \u201cthree Vs\u201d:<\/a><\/p>\n\n\n\n