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A review by shelving
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are by Seth Stephens-Davidowitz
3.0
should've just wrote an extended new yorker article or some shit.
seriously.
seriously.
i guess, to use this example, what i'd expected is a breakdown on why knowing american men are gay is important. cast allusions to the way of life, to the economy, to the bias towards heterosexual nuclear families as fundamental to the functioning of capitalism as we know it. anything but "haha 4.8% of men in texas search for gay porn!! way higher than rhode island!! but facebook surveys say only 2% watch gay porn!!". though subsequently he does address this via his thesis statement: that google searches are a more honest way of obtaining data. women who suspect their husbands are gay (and of course, this only applies to closeted gay/bi/pan/what have you men) do sometimes google "how do i know my husband likes dick?".
what i do enjoy about this book is him introducing inventive ways of obtaining data. what do you look at vs. what do you don't? how do you avoid from making correlation/causation assumptions? he introduces a number of pitfalls (coin 372) that i've never heard of when it comes to data analysis, and that was fascinating. but he lets his """humorous personality""" (his words, not mine) eclipse the data side of this and it sucks. for example:
conclusion? this dude needs to get on more qual studies.
Spoiler
i'm beginning to think my issue is just reading books by white dudes, whose fundamental perspective in life is narrow as fuck and differs greatly from mine (also see: my take on what could b a phenom sf novel but is sadly impeded by white guyism) and that i should probably stop reading books authored by people like these. but you never know, do you? you never know if you'll end up with a pratchett or if you find yourself mid-way through a book about big data and google searches and having to read the line "How many American men are gay? This is a legendary question in sexuality research".seriously.
seriously.
i guess, to use this example, what i'd expected is a breakdown on why knowing american men are gay is important. cast allusions to the way of life, to the economy, to the bias towards heterosexual nuclear families as fundamental to the functioning of capitalism as we know it. anything but "haha 4.8% of men in texas search for gay porn!! way higher than rhode island!! but facebook surveys say only 2% watch gay porn!!". though subsequently he does address this via his thesis statement: that google searches are a more honest way of obtaining data. women who suspect their husbands are gay (and of course, this only applies to closeted gay/bi/pan/what have you men) do sometimes google "how do i know my husband likes dick?".
what i do enjoy about this book is him introducing inventive ways of obtaining data. what do you look at vs. what do you don't? how do you avoid from making correlation/causation assumptions? he introduces a number of pitfalls (coin 372) that i've never heard of when it comes to data analysis, and that was fascinating. but he lets his """humorous personality""" (his words, not mine) eclipse the data side of this and it sucks. for example:
Perhaps someone in the president’s office had read Soltas’s and my Times column, which discussed what had worked and what didn’t. For the content of this speech was noticeably different.
When people learn that I am a data scientist and a writer, they sometimes will share some fact or survey with me. I often find this data boring—static and lifeless. It has no story to tell. Likewise, friends have tried to get me to join them in reading novels and biographies. But these hold little interest for me as well. I always find myself asking, “Would that happen in other situations? What’s the more general principle?” Their stories feel small and unrepresentative.
conclusion? this dude needs to get on more qual studies.