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賴賲賴 丿乇賵睾 賲蹖鈥屭堐屬嗀�
欧宝娱乐 Choice Award
Nominee for Readers' Favorite Science & Technology (2017)丨乇賮 丕氐賱蹖 丕蹖賳 讴鬲丕亘 丕蹖賳 蹖讴 噩賲賱賴 丕爻鬲: 芦賲丕 亘賴 賴賲賴 丿乇賵睾 賲蹖鈥屭堐屰屬� 亘賴 噩夭 噩爻鬲噩賵诏乇 诏賵诏賱禄. 丿丕賵蹖丿賵賵蹖鬲夭 丿乇 丕蹖賳 讴鬲丕亘 丿乇亘丕乇賴 丕賳亘賵賴 丕胤賱丕毓丕鬲蹖 讴賴 卮乇讴鬲鈥屬囏� 亘夭乇诏 亘賴 賵蹖跇賴 诏賵诏賱 丕夭 賲乇丿賲 噩賲毓 賲蹖鈥屭┵嗁嗀� 賴賲丕賳 Big Data 賵 讴丕乇亘乇丿 丌賳賴丕 丿乇 賲胤丕賱毓丕鬲 噩丕賲毓賴鈥屫促嗀ж池з嗁� 賵 乇賵丕賳鈥屫促嗀ж池з嗁� 賵 卮賳丕爻丕蹖蹖 乇賮鬲丕乇賴丕 賵 毓丕丿丕鬲 噩賵丕賲毓 賲禺鬲賱賮 氐丨亘鬲 賲蹖鈥屭┵嗀�. 丕賵 丿乇 丕蹖賳 丕孬乇 亘賴 賲賵囟賵毓 噩丕賱亘蹖 丕卮丕乇賴 賲蹖 讴賳丿 賵 丌賳 賴賲 丕蹖賳 丕爻鬲 讴賴 賲丕 賲賲讴賳 丕爻鬲 丿乇亘丕乇賴 賲賵囟賵毓蹖 讴賴 匕賴賳賲丕賳 乇丕 賲卮睾賵賱 讴乇丿賴 賲孬賱丕 蹖讴 丿賱 倬蹖趩賴 卮亘丕賳賴 亘賴 卮乇蹖讴 夭賳丿诏蹖鈥屬呚з� 趩蹖夭蹖 賳诏賵蹖蹖賲 丕賲丕 丨鬲賲丕 亘賴 爻乇丕睾 诏賵诏賱 賲蹖 乇賵蹖賲 鬲丕 亘亘蹖賳蹖賲 丿乇丿賲丕賳 賲賲讴賳 丕爻鬲 賳卮丕賳賴 趩賴 趩蹖夭蹖 亘丕卮丿. 賲丕 賲賲讴賳 丕爻鬲 亘丕 丕胤乇丕賮蹖丕賳賲丕賳 氐丕丿賯 賳亘丕卮蹖賲 丕賲丕 丕夭 诏賵诏賱 賳賲蹖鈥屫堌з嗃屬� 趩蹖夭蹖 乇丕 賲禺賮蹖 讴賳蹖賲 丿乇 賳鬲蹖噩賴 诏賵诏賱 鬲亘丿蹖賱 亘賴 丕賳亘丕乇 亘夭乇诏蹖 丕夭 丿丕丿賴鈥屬囏й� 乇賮鬲丕乇蹖 丕賳爻丕賳鈥屬囏� 賲蹖鈥屫促堌� 讴賴 賲蹖鈥屫堌з� 丿乇 鬲丨賱蹖賱鈥屬囏й� 亘夭乇诏 毓賱賲蹖 丿乇 乇卮鬲賴鈥屬囏й� 賲禺鬲賱賮 丕夭 丌賳賴丕 丕爻鬲賮丕丿賴 讴乇丿.
丿乇 丕蹖賳 讴鬲丕亘 賳賵蹖爻賳丿賴 亘乇丕蹖 丕孬亘丕鬲 丕蹖賳 丨乇賮 禺賵丿貙 賲孬丕賱鈥屬囏ж� 丿丕爻鬲丕賳鈥屬囏� 賵 賲氐丕丿蹖賯 诏賵賳丕诏賵賳 賵 噩丕賱亘蹖 賲蹖鈥屫①堌必� 讴賴 卮賳蹖丿賳 賵 禺賵丕賳丿卮丕賳 亘乇丕蹖 賴賲賴 亘賴 禺氐賵氐 讴丕乇亘乇丕賳 賴賲蹖卮诏蹖 丕蹖賳鬲乇賳鬲 噩匕丕亘 禺賵丕賴丿 亘賵丿.
丿乇 丕蹖賳 讴鬲丕亘 賳賵蹖爻賳丿賴 亘乇丕蹖 丕孬亘丕鬲 丕蹖賳 丨乇賮 禺賵丿貙 賲孬丕賱鈥屬囏ж� 丿丕爻鬲丕賳鈥屬囏� 賵 賲氐丕丿蹖賯 诏賵賳丕诏賵賳 賵 噩丕賱亘蹖 賲蹖鈥屫①堌必� 讴賴 卮賳蹖丿賳 賵 禺賵丕賳丿卮丕賳 亘乇丕蹖 賴賲賴 亘賴 禺氐賵氐 讴丕乇亘乇丕賳 賴賲蹖卮诏蹖 丕蹖賳鬲乇賳鬲 噩匕丕亘 禺賵丕賴丿 亘賵丿.
296 pages, Unknown Binding
First published May 9, 2017
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April 27, 2017
This book tries too hard to be Freakonomics. The first two parts are full of random examples of interesting but mostly pointless things that can learned via Google search trends. However, a whole lot of assumptions are made off these bits of data that don't seem to have much basis in factual scientific methods of research. Unprofessional jokes are thrown in randomly. If you need a footnote to explain why a joke was not homophobic maybe you should have just skipped the joke. And any book of less than 300 pages of text should not need to use the same example three times, especially when it's about how the author can't believe women are concerned about the smell of their vagina.
The last section of the book explains the limitations big data holds and is really the most grounded section, the rest being almost hagiography. It would have done a lot to work the third section into the examples of the first two sections. It would have balanced out the praise and also would have done much to explain the flaws present in some of the examples included.
Some cool facts buried in a lot of murky oddness.
Disclaimer: I was given this book in a 欧宝娱乐 giveaway.
The last section of the book explains the limitations big data holds and is really the most grounded section, the rest being almost hagiography. It would have done a lot to work the third section into the examples of the first two sections. It would have balanced out the praise and also would have done much to explain the flaws present in some of the examples included.
Some cool facts buried in a lot of murky oddness.
Disclaimer: I was given this book in a 欧宝娱乐 giveaway.
May 6, 2021
鈥eople鈥檚 search for information is, in itself, information. When and where they search for facts, quotes, jokes, places, persons, things, or help, it turns out, can tell us a lot more about what they really think, really desire, really fear, and really do than anyone might have guessed. This is especially true since people sometimes don鈥檛 so much query Google as confide in it: 鈥淚 hate my boss.鈥� 鈥淚 am drunk.鈥� 鈥淢y dad hit me.鈥�There鈥檚 lies, damned lies and then there are statistics. One must wonder. Do the lies get bigger as the datasets grow? Seth Stephens-Davidowitz posits that the availability of vast sums of new data not only allows researchers to make better predictions, but offers them never-before-available tools that can offer insight that direct questioning never could.
We have seen steps up of this type before. Malcolm Gladwell has made a career of such, with Blink, Outliers, and The Tipping Point. Freakonomics is the one I would expect most folks would know. Nate Silver put his data expertise into The Signal and the Noise. All these looks at data and how we interpret it rely on the analyst, regardless, pretty much, of the data. While the same might be true of Stephens-Davidowitz鈥檚 approach, he focuses on the availability of materials that have not been there in the past. The smarts that must be applied to get the most interesting results can now be applied to new oceans of data. It is more possible than it has ever been to draw inferences and actually test them out.
In addition to the volume of data that is now available, there is the sort. The author looks at Google and FB data for evidence of underlying realities. Surveys can sometimes offer inaccurate outcomes, when the people being queried do not provide honest answers. Are you a racist? Yes/No. But one can look at what people enter into Google to get a sense of possible racism by geographic area. The everyday act of typing a word or phrase into a compact rectangular white box leaves a small trace of truth that, when multiplied by millions, eventually reveals profound realities. Looking for queries on jokes involving the N-Word, for example, turns out to yield a telling portrait of anti-black sentiment, which also correlates with lower black life expectancy. (And pro-Trump vote totals)
We are treated to looks into a variety of research subjects, from picking the ponies, to seeing what really interests/concerns people sexually, looking for patterns of child abuse, selecting the best wine, using the texts of a vast number of books and movie scripts to come up with six simple plot structures.
I thought the most interesting piece was on the use of associations, and provoking curiosity, rather than relying on overt statements to influence how people feel about a different group of people. Another was on using a data comparison of one鈥檚 (anonymous) medical information to others who share many characteristics to improve medical diagnoses.
There are some areas in which it was not entirely persuasive that the methodology in question was tracking what was claimed. SS-D sees in searches of Pornhub, for example, what people really want and really do, not what they say they want and say they do. Really? I expect that what people check out on-line does not necessarily track with what might be of interest in real life. It would be like someone with an interest in mysteries being thought to have homicidal tendencies after searching for a variety of homicide related titles. Should a writer doing research into a dark subject like child pornography, human trafficking or cannibalism expect the heavy knock of the police on his/her door? Where is the line between an academic or titillation search and one made for planning?
SS-D makes a point about there being a significant difference between searches that offer projections for groups or areas, and their inapplicability for predicting individual behavior, although that will not necessarily remain the case. In baseball, for example, the explosion of available information may very well be applied to specific players to diagnose and even correct flaws in technique, or recognize patterns that might expose underlying medical issues, or predict their arrival. The Big Data related here is much more macro, looking at group proclivities. Useful for spotting trends, measuring public sentiment, but in more detail than has been heretofore possible.
And of course there is the impact of dark players. Those with the resources and motivation could manipulate the Big Data produced by Google and Facebook. Such players would not necessarily be limited to Russian cyber-spies and pranksters, but corporate and ideological players as well, like . There could have been a bit more in here on those concerns.
The book offers plenty of anecdotal bits that could have been lifted from any of the other data books noted at the top of this review. What one needs, ultimately is smart, insightful analysis. Having all the data in the world (that means you, NSA) is merely a burden unless there is someone insightful enough to figure out the right questions to ask, and how to ask them.
SS-D notes several Google (Trends, Ngrams, Correlate) services that might be familiar to folks doing actual research, but which were news to me. It might be useful to check out some of these, maybe even come up with meaningful queries to shed light on pressing, or even completely frivolous questions.
Not all problems can be solved, or even examined by the addition of ever more data. Sometimes, many times, the information that is available is perfectly sufficient to the task, but other factors prevent the joining together of its various pieces to create a meaningful whole. The now classic example is from 9/11, when an absence of coordination between the CIA and FBI resulted in suicide bombers who could have been foiled succeeding in their mission. Politics and the culture of nations and organizations figure into how data is used
So if everybody lies, is Seth Stephens-Davidowitz telling us the truth? I am sure there is a query one could construct that would look at diverse data sources, pull them all together and give us a fuller picture, but for now, we will have to make do with reading his book and articles, checking out his videos, applying the analytical tools already incorporated into our brains, and seeing if there is enough information there with which to come to a well-grounded conclusion. And that鈥檚 no lie.
Review first posted 鈥� May 5, 2017
Publication date 鈥� May 9, 2017
=============================EXTRA STUFF
Links to the author鈥檚 , , and pages
VIDEOS 鈥� SS-D speaking
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----- - Arts & Ideas at the JCCSF
----- - The Julis-Rabinowitz Center for Public Policy and Finance
The June 2017 National Geographic cover story has particular relevance to the treatment of actual truth in today's political environment. It is illuminating, if not exactly uplifting. - - By Yudhijit Bhattacharjee
July 12, 2017 - Washington Post - one of the very serious applications of big data - - by Philip Bump
July 15, 2017 - One of the ways big data gets compromised is via automated dishonesty - by Tim Wu - Thanks to Henry B for letting us know about the article
February 3, 2018
When sociologist ask people if they waste food, people give the only correct answer. It's wrong to waste food.
When sociologist survey the contents of the same people's garbage, they get a more accurate answer.
Just imagine how much more information is available trolling through internet searches.
When sociologist survey the contents of the same people's garbage, they get a more accurate answer.
Just imagine how much more information is available trolling through internet searches.
June 18, 2023
2020 EXHORTATION Wednesday, 29 July 2020, the four horse-manuremen of the datapocalypse will testify before Congress about their insane, untrammeled greed and its deleterious effect on Society. (I am presupposing the end result of the hearing here because I am under no obligation to hide my own opinion of these nauseating monopolists.)
2019 EXHORTATION We're entering the 2020 election cycle for real at this moment. Please, all US citizens, PLEASE read books! Especially books about data, how it's acquired and analyzed, how it's massaged and manipulated鈥攖he more you know about the topic, the harder it will be for agenda-having politicians to lie to you with numbers.
I have nothing unique to add to the conversation about this book. I think those most in need of reading it won't, and that's frustrating.
If you've ever seen a number adduced to explain a trend, read this book. If you've ever asserted that a certain percentage of something was something/something else, read this book. If you've ever seen a politician quote a study and your innate bullshit filter clogged up, read this book.
Really simple, high-level terms: READ. THIS. BOOK.
2019 EXHORTATION We're entering the 2020 election cycle for real at this moment. Please, all US citizens, PLEASE read books! Especially books about data, how it's acquired and analyzed, how it's massaged and manipulated鈥攖he more you know about the topic, the harder it will be for agenda-having politicians to lie to you with numbers.
I have nothing unique to add to the conversation about this book. I think those most in need of reading it won't, and that's frustrating.
If you've ever seen a number adduced to explain a trend, read this book. If you've ever asserted that a certain percentage of something was something/something else, read this book. If you've ever seen a politician quote a study and your innate bullshit filter clogged up, read this book.
Really simple, high-level terms: READ. THIS. BOOK.
January 22, 2018
This is an engaging book about how big data can be used to improve our understanding of human behavior, thinking, emotions, and preference. The basic idea is that if you ask people about their behavior or their preferences in surveys, even anonymous surveys, they will often lie. People do not like to admit to low-brow preferences; racists do not want to admit to their prejudices, most people who watch pornography do not want to admit to it, and even voting is often misrepresented; some people who voted for Trump would not admit to it.
But, by analyzing immense datasets from Google, public archives, social media, and the like, Seth Stephens-Davidowitz has been able to unearth a lot of fascinating answers to puzzling questions. For example, he is able to predict, through Google searches for various symptoms, who is likely to have early stages of pancreatic cancer. He can predict epidemic breakouts of some contagious diseases well before they are announced by the CDC (Center for Disease Control). He shows that the single factor that correlates with voting for Trump is that of racism.
Then there are the fun factoids, about the sorts of things that people search for most often on Google. Most commonly, the search "Is my son ..." is followed by "gifted", while the search "Is my daughter ..." is followed by "overweight". That tells us something about stereotypes for the way people think about their children. Interestingly, the release of a new violent movie in a city is correlated with a decrease in violent crime in that city. Perhaps the reason is that violent people who are watching the movie are not out on the streets, committing crimes.
And here we get to the main problem with this sort of analysis. Undoubtedly, the research and analysis of big datasets is done correctly. However, once a surprising result is found, understanding the motivations behind the online activity are often subjective and open to interpretation. While this book is very careful about its underlying assumptions, it is a slippery road to getting the correct interpretations and explanations.
This is an easy, well-paced book that should appeal to anybody who enjoys books like .
But, by analyzing immense datasets from Google, public archives, social media, and the like, Seth Stephens-Davidowitz has been able to unearth a lot of fascinating answers to puzzling questions. For example, he is able to predict, through Google searches for various symptoms, who is likely to have early stages of pancreatic cancer. He can predict epidemic breakouts of some contagious diseases well before they are announced by the CDC (Center for Disease Control). He shows that the single factor that correlates with voting for Trump is that of racism.
Then there are the fun factoids, about the sorts of things that people search for most often on Google. Most commonly, the search "Is my son ..." is followed by "gifted", while the search "Is my daughter ..." is followed by "overweight". That tells us something about stereotypes for the way people think about their children. Interestingly, the release of a new violent movie in a city is correlated with a decrease in violent crime in that city. Perhaps the reason is that violent people who are watching the movie are not out on the streets, committing crimes.
And here we get to the main problem with this sort of analysis. Undoubtedly, the research and analysis of big datasets is done correctly. However, once a surprising result is found, understanding the motivations behind the online activity are often subjective and open to interpretation. While this book is very careful about its underlying assumptions, it is a slippery road to getting the correct interpretations and explanations.
This is an easy, well-paced book that should appeal to anybody who enjoys books like .
December 16, 2019
亘賳馗乇賲 讴鬲丕亘 亘蹖鈥屬嗀港屫臂� 亘賵丿. 賲賳 丕胤賱丕毓 丿賯蹖賯蹖 丕夭 讴賱丕賳鈥屫ж団€屬囏� 賳丿丕卮鬲賲 賵 賴賲賵賳 趩蹖夭賴丕蹖蹖 乇賵 賲蹖鈥屫堎嗀池� 讴賴 丕讴孬乇丕賸 賲蹖鈥屫堎嗃屬�. 賵賱蹖 丕蹖賳 讴鬲丕亘 丿蹖丿 禺蹖賱蹖 噩丕賱亘鈥屫臂� 亘賴賲 丿丕丿. 丕賱丕賳 讴賲蹖 亘蹖卮鬲乇貙 丕夭 丕蹖賳鬲乇賳鬲 賵 丕胤賱丕毓丕鬲蹖 讴賴 丕夭賲 亘賴 噩丕 賲蹖鈥屬呝堎嗁� 賲蹖鈥屫必迟呚� 蹖讴賲 亘蹖卮鬲乇 丨賵丕爻賲 乇賵 噩賲毓 賲蹖鈥屭┵嗁呚� (丕賳诏丕乇 讴賴 趩卮賲 爻賵賲蹖 賲丿丕賲 丿乇丨丕賱 賲卮丕賴丿賴 鬲賵爻鬲. 賳賴 亘賴 氐賵乇鬲 卮禺氐蹖貙 讴賴 蹖讴 爻賵跇賴 賳丕卮賳丕爻. 讴賴 鬲乇爻賳丕讴鈥屫� 賴賲 賴爻鬲.) 賵 鈥屫簇ж┾€屬囏з� 賳爻亘鬲 亘賴 趩蹖夭賴丕蹖蹖 讴賴 賲蹖鈥屫ㄛ屬嗁� 丨爻丕爻鈥屫� 卮丿賳.
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亘賳丕亘乇丕蹖賳 讴鬲丕亘 乇丕 亘賴 卮丕蹖爻鬲賴鈥屫臂屬� 卮讴賱 賲賲讴賳 亘賴 倬丕蹖丕賳 賲蹖鈥屫ㄘ辟呚� 蹖毓賳蹖 亘丕 鬲亘毓蹖鬲 丕夭 丿丕丿賴鈥屬囏� 賵 鬲賵噩賴 亘賴 讴丕乇蹖 讴賴 賲乇丿賲 賲蹖鈥屭┵嗁嗀� 賳賴 趩蹖夭蹖 讴賴 賲蹖鈥屭堐屬嗀�. 倬爻 賲蹖鈥屫辟堎� 亘丕 趩賳丿鬲丕 丕夭 丿賵爻鬲丕賳賲 賳賵卮蹖丿賳蹖 賲蹖鈥屫堌辟� 賵 丿爻鬲 丕夭 讴丕乇 讴乇丿賳 乇賵蹖 丕蹖賳 賲丐禺乇賴 賱毓賳鬲蹖 賲蹖鈥屭┴促�.
讴賱丕賳鈥屫ж団€屬囏� 賲蹖鈥屭堐屬嗀� 鬲賳賴丕 鬲毓丿丕丿 禺蹖賱蹖 讴賲蹖 丕夭 卮賲丕 賴賳賵夭 丿乇 丨丕賱 禺賵丕賳丿賳蹖丿.禄
噩丕賱亘 丕蹖賳讴賴貙 賲賳 賲賯丿賲賴 乇賵 讴賴 禺賵賳丿賴 亘賵丿賲 賵 亘乇丕蹖 蹖讴蹖 丕夭 丿賵爻鬲丕賲 鬲毓乇蹖賮卮 賲蹖鈥屭┴必� 诏賮鬲 禺亘 丿蹖诏賴 賲蹖鈥屫堌й� 亘禺賵賳蹖 趩蹖讴丕乇責 賴賲蹖賳賴 丿蹖诏賴. 賮賴賲蹖丿蹖 趩賴 禺亘乇賴...
賵賱蹖 賴蹖. 賲賳 鬲丕 丌禺乇 禺賵賳丿賲卮. 亘爻蹖丕乇 賱匕鬲 亘乇丿賲. 亘爻蹖丕乇 鬲丨鬲鈥屫Ｘ屫� 丕蹖賳 賴賲賴 讴丕乇 賵 夭丨賲鬲蹖 讴賴 讴卮蹖丿蹖 賯乇丕乇 诏乇賮鬲賲 賵 丿賱賲 禺賵丕爻鬲 禺賵丿賲 賴賲 丿乇 丕賳噩丕賲卮賵賳 亘賴鬲 讴賲讴 賲蹖鈥屭┴必�. 賲賲賳賵賳.
夭亘丕賳 胤賳丕夭 賵 丿乇毓蹖賳鈥屫з� 睾賲鈥屫з嗂屫槽� 丿丕卮鬲 讴賴 亘丕毓孬 賲蹖鈥屫簇� 亘丕 丕蹖賳讴賴 賲孬賱丕賸 賳丕賳鈥屬佦屭┴促嗁囏� 乇丕丨鬲 賵 乇賵賵賳 亘丕卮賴 賵 鬲賵賳爻鬲賴 亘賵丿 賲胤丕賱毓丕鬲 爻禺鬲 丌丿賲鈥屬囏� 乇賵 亘賴 卮讴賱蹖 讴賴 賴乇 禺賵丕賳賳丿賴鈥屫й� 賲鬲賵噩賴卮賵賳 亘卮賴 鬲賵囟蹖丨 亘丿賴貙 讴賴 禺蹖賱蹖 亘丕毓孬 鬲丨鬲鈥屫Ｘ屫辟傌必ж壁辟佖嗁� 卮丿.
鬲乇噩賲賴 賴賲 鬲乇噩賲賴 禺賵亘蹖 亘賵丿. 诏乇趩賴 蹖讴 夭蹖乇賮氐賱 (亘禺卮 丕賵賱 賮氐賱 趩賴丕乇賲: The truth about sex) 丨匕賮 卮丿賴 讴賴 賲賳 丕夭 乇賵蹖 賳爻禺賴 丕賳诏賱蹖爻蹖卮 禺賵賳丿賲 賵 丕賵賳賲 禺蹖賱蹖 噩丕賱亘 賵 丌诏丕賴蹖鈥屫ㄘ� 亘賵丿. 讴丕卮讴蹖 趩丕倬 賲蹖鈥屫簇�.
丕賲丕 丿乇賳賴丕蹖鬲 毓丕卮賯 倬丕乇丕诏乇丕賮 丌禺乇 賲丐禺乇賴 卮丿賲 讴賴 诏賮鬲賴 亘賵丿:
芦丕賲丕 賲賴賲 賳蹖爻鬲 趩賯丿乇 亘乇丕蹖 倬蹖乇丕爻鬲賳 賳孬乇賲 夭丨賲鬲 讴卮蹖丿賴 亘丕卮賲貙 趩乇丕 讴賴 亘蹖卮鬲乇 賲乇丿賲 賳賴丕蹖鬲丕賸 倬賳噩丕賴 氐賮丨賴 丕賵賱 乇丕 賲蹖鈥屫堌з嗁嗀� 趩賳丿 賳讴鬲賴 乇丕 賲蹖鈥屬佡囐呝嗀� 賵 亘毓丿 亘賴 夭賳丿诏蹖鈥屫簇з� 丕丿丕賲賴 賲蹖鈥屫囐嗀�.
亘賳丕亘乇丕蹖賳 讴鬲丕亘 乇丕 亘賴 卮丕蹖爻鬲賴鈥屫臂屬� 卮讴賱 賲賲讴賳 亘賴 倬丕蹖丕賳 賲蹖鈥屫ㄘ辟呚� 蹖毓賳蹖 亘丕 鬲亘毓蹖鬲 丕夭 丿丕丿賴鈥屬囏� 賵 鬲賵噩賴 亘賴 讴丕乇蹖 讴賴 賲乇丿賲 賲蹖鈥屭┵嗁嗀� 賳賴 趩蹖夭蹖 讴賴 賲蹖鈥屭堐屬嗀�. 倬爻 賲蹖鈥屫辟堎� 亘丕 趩賳丿鬲丕 丕夭 丿賵爻鬲丕賳賲 賳賵卮蹖丿賳蹖 賲蹖鈥屫堌辟� 賵 丿爻鬲 丕夭 讴丕乇 讴乇丿賳 乇賵蹖 丕蹖賳 賲丐禺乇賴 賱毓賳鬲蹖 賲蹖鈥屭┴促�.
讴賱丕賳鈥屫ж団€屬囏� 賲蹖鈥屭堐屬嗀� 鬲賳賴丕 鬲毓丿丕丿 禺蹖賱蹖 讴賲蹖 丕夭 卮賲丕 賴賳賵夭 丿乇 丨丕賱 禺賵丕賳丿賳蹖丿.禄
噩丕賱亘 丕蹖賳讴賴貙 賲賳 賲賯丿賲賴 乇賵 讴賴 禺賵賳丿賴 亘賵丿賲 賵 亘乇丕蹖 蹖讴蹖 丕夭 丿賵爻鬲丕賲 鬲毓乇蹖賮卮 賲蹖鈥屭┴必� 诏賮鬲 禺亘 丿蹖诏賴 賲蹖鈥屫堌й� 亘禺賵賳蹖 趩蹖讴丕乇責 賴賲蹖賳賴 丿蹖诏賴. 賮賴賲蹖丿蹖 趩賴 禺亘乇賴...
賵賱蹖 賴蹖. 賲賳 鬲丕 丌禺乇 禺賵賳丿賲卮. 亘爻蹖丕乇 賱匕鬲 亘乇丿賲. 亘爻蹖丕乇 鬲丨鬲鈥屫Ｘ屫� 丕蹖賳 賴賲賴 讴丕乇 賵 夭丨賲鬲蹖 讴賴 讴卮蹖丿蹖 賯乇丕乇 诏乇賮鬲賲 賵 丿賱賲 禺賵丕爻鬲 禺賵丿賲 賴賲 丿乇 丕賳噩丕賲卮賵賳 亘賴鬲 讴賲讴 賲蹖鈥屭┴必�. 賲賲賳賵賳.
December 31, 2019
such an interesting book, it broaden my views, i'm looking forward to read more books of the author
June 16, 2019
Everybody Lies has all the makings of the kind of book I get suckered into buying during an amazon kindle sale. A pop culture polemic that has a very short half-life of relevancy. After reading it, my first blush was to say that I was spot on. But as I thought about it, I realized it had more depth. That's likely because Seth Stephens-Davidowitz is an actual scientist trying to educate people about what they are actually revealing with everything that they say and do.
The late 20th Century has heralded access to vast quantities of information on every one of us. Our buying habits, browsing habits, what news sources that we use in a very proliferated world of news access. We are telling about ourselves every time we go online on our tablets, phones and computers. Every text, every phone call, every e-mail is adding data to our digital make up. Like it or not, data is collected and available for each and every one of us on some very personal things. There is a field of science dedicated to analyzing the data and interpreting its meaning. The byproducts of this new field are used for good and evil. Corporations can use the information to target people who may buy their items (How did goodreads know that I was thinking about buying a mattress?). Some data mining results could be used to determine how much people need certain types of government services. Some internet searches combined with buying habits, forum discussions, book reviews, blog posts etc have led to medical discoveries. The amount of data is staggering and the ability to compile and analyze the data to reveal useful information is a new science that goes way beyond statistics. It requires knowledge of math, and sociology and psychology and engineering and biological science and an understanding of human nature etc to attempt to mine useful information. What Stephens-Davidowitz has discovered is that everybody lies about鈥ell everything. His primary discussion is that people rarely tell the truth in poll and surveys etc. They also lie on their online data habits. Oftentimes to themselves. That little fact obviously complicates the mining of data for example in Red States w/ their stated evangelical postures that consume the most porn and have the highest rates of internet searches for access to abortions etc. People lie in their own searches as they seek to reinforce their own positions and don't necessarily search for answers. Those kinds of actions are not surprising but they complicate analysis (understatement).
This book was very interesting data primer. There is so much more to the amount of data most of us generate every day and Stephens-Davidowitz does a great job of explaining the basics. Some of his examples and his approach are a bit superficial, juvenile, pop culture. I don't find myself curious about the users of pornhub, or average penis size or baseball stats. Some of that was silly and salacious; betraying his youth and blatantly catering to what his data mining perceived would be an audience of young males. Bah. Also, he quoted Malcolm Gladwell as a resource which in my view should never be used if you hope to build a foundation based upon experience in the field and credibility on the subject鈥f anything. Nonetheless, I enjoyed the book and I think Stephens-Davidowitz has a very compelling and prosperous future as both a scientist and a writer.
4 Stars
Read on kindle
The late 20th Century has heralded access to vast quantities of information on every one of us. Our buying habits, browsing habits, what news sources that we use in a very proliferated world of news access. We are telling about ourselves every time we go online on our tablets, phones and computers. Every text, every phone call, every e-mail is adding data to our digital make up. Like it or not, data is collected and available for each and every one of us on some very personal things. There is a field of science dedicated to analyzing the data and interpreting its meaning. The byproducts of this new field are used for good and evil. Corporations can use the information to target people who may buy their items (How did goodreads know that I was thinking about buying a mattress?). Some data mining results could be used to determine how much people need certain types of government services. Some internet searches combined with buying habits, forum discussions, book reviews, blog posts etc have led to medical discoveries. The amount of data is staggering and the ability to compile and analyze the data to reveal useful information is a new science that goes way beyond statistics. It requires knowledge of math, and sociology and psychology and engineering and biological science and an understanding of human nature etc to attempt to mine useful information. What Stephens-Davidowitz has discovered is that everybody lies about鈥ell everything. His primary discussion is that people rarely tell the truth in poll and surveys etc. They also lie on their online data habits. Oftentimes to themselves. That little fact obviously complicates the mining of data for example in Red States w/ their stated evangelical postures that consume the most porn and have the highest rates of internet searches for access to abortions etc. People lie in their own searches as they seek to reinforce their own positions and don't necessarily search for answers. Those kinds of actions are not surprising but they complicate analysis (understatement).
This book was very interesting data primer. There is so much more to the amount of data most of us generate every day and Stephens-Davidowitz does a great job of explaining the basics. Some of his examples and his approach are a bit superficial, juvenile, pop culture. I don't find myself curious about the users of pornhub, or average penis size or baseball stats. Some of that was silly and salacious; betraying his youth and blatantly catering to what his data mining perceived would be an audience of young males. Bah. Also, he quoted Malcolm Gladwell as a resource which in my view should never be used if you hope to build a foundation based upon experience in the field and credibility on the subject鈥f anything. Nonetheless, I enjoyed the book and I think Stephens-Davidowitz has a very compelling and prosperous future as both a scientist and a writer.
4 Stars
Read on kindle
December 22, 2022
爻鬲 丕蹖夭丕讴 丕爻鬲蹖賵賳夭 丿蹖賵蹖丿賵蹖鬲爻 賳賵蹖爻賳丿賴 賵 丿丕賳卮賲賳丿 噩賵丕賳 丌賲乇蹖讴丕蹖蹖 丕毓鬲賯丕丿 丿丕乇丿 讴賴 丕氐賵賱丕 丕賳爻丕賳 賴丕 亘禺卮蹖 丕夭 賳馗乇丕鬲 賵 乇賮鬲丕乇 禺賵丿 乇丕 倬賳賴丕賳 讴乇丿賴 蹖丕 馗丕賴乇 爻丕夭蹖 賲蹖 讴賳賳丿 ( 丿乇 賮乇賴賳诏 賲丕 囟乇亘 丕賱賲孬賱 賴丕蹖蹖 賲丕賳賳丿 禺賵丕賴蹖 賳卮賵蹖 乇爻賵丕 賴賲乇賳诏 噩賲丕毓鬲 卮賵 蹖丕 亘丕 爻蹖賱蹖 氐賵乇鬲 禺賵丿 乇丕 爻乇禺 賳诏賴 丿丕卮鬲賳 賴賲 鬲丕讴蹖丿蹖 丕蹖爻鬲 亘乇 賴賲蹖賳 賲賮賴賵賲 貙 蹖毓賳蹖 鬲賲丕蹖夭 賲蹖丕賳 馗丕賴乇 賵 亘丕胤賳 蹖丕 鬲賯丕亘賱 賲蹖丕賳 丿乇賵賳 賵 亘蹖乇賵賳 . 丿乇賵賳 丕爻鬲 讴賴 丕乇夭卮 夭蹖丕丿蹖 丿丕乇丿 賵 亘蹖乇賵賳 亘丕 丌賳讴賴 丨丕卅賱蹖 丕蹖爻鬲 亘乇丕蹖 丿賳蹖丕蹖 丨爻丕爻 賵 馗乇蹖賮 亘丕胤賳 丕賲丕 亘乇 丌賳 鬲氐賳毓 賵 丕丨鬲蹖丕胤 丨丕讴賲 丕爻鬲 ) . 亘賳丕亘乇丕蹖賳 賳馗乇 爻賳噩蹖 賴丕 賴賲丕賳賳丿 丕亘夭丕乇賴丕蹖 爻胤丨蹖 賴爻鬲賳丿 讴賴 賳賲蹖 鬲賵丕賳賳丿 亘賴 毓賲賯 乇賮鬲丕乇 丕賳爻丕賳 倬蹖 亘亘乇賳丿 丕賲丕 亘丕 诏爻鬲乇卮 乇賵夭丕賮夭賵賳 丕蹖賳鬲乇賳鬲 賵 丕爻鬲賮丕丿賴 诏乇賵賴 夭蹖丕丿蹖 丕夭 賲乇丿賲 丕夭 丌賳 貙 丨丕賱 賲蹖 鬲賵丕賳 丿乇蹖丕賮鬲 讴賴 賲乇丿賲 丿乇 噩爻鬲 賵 噩賵蹖 乇賵夭丕賳賴 卮丕賳 亘賴 丿賳亘丕賱 趩賴 賴爻鬲賳丿 貙 趩賴 趩蹖夭賴丕蹖蹖 乇丕 丿賵爻鬲 丿丕乇賳丿 賵 蹖丕 丨鬲蹖 亘賴 讴丿丕賲 讴丕賳丿蹖丿丕 乇丕蹖 禺賵丕賴賳丿 丿丕丿 . 丕蹖賳 禺賱丕氐賴 賵 丕爻丕爻 讴鬲丕亘 賴賲賴 丿乇賵睾 賲蹖 诏賵蹖賳丿 賳賵卮鬲賴 爻鬲 丕爻鬲蹖賵賳夭 丕爻鬲 .
賳賵蹖爻賳丿賴 乇賵卮賴丕蹖蹖 賲丕賳賳丿 胤乇丕丨蹖 倬乇爻卮 賳丕賲賴 蹖丕 爻賵丕賱 讴乇丿賳 賲爻鬲賯蹖賲 丕夭 賲乇丿賲 乇丕 賳丕讴丕乇賲丿 賵 賲賳爻賵禺 賲蹖 丿丕賳丿 貙 丕賵 亘賴 噩丕蹖 丕蹖賳 乇賵卮 丕爻鬲賮丕丿賴 丕夭 讴賱丕賳 丿丕丿賴 蹖丕 亘蹖诏 丿蹖鬲丕 乇丕 倬蹖卮賳賴丕丿 賲蹖 讴賳丿 讴賴 賴乇 賱丨馗賴 亘乇 丨噩賲 丌賳 丕賮夭賵丿賴 賲蹖 卮賵丿 . 亘賴 丕蹖賳 诏賵賳賴 丿丕賳卮賲賳丿丕賳 毓賱賵賲 丕噩鬲賲丕毓蹖 亘賴 丕賯蹖丕賳賵爻蹖 丕夭 丕胤賱丕毓丕鬲 丿爻鬲 倬蹖丿丕 讴乇丿賴 丕賳丿 讴賴 鬲賯乇蹖亘丕 鬲賲丕賲蹖 賳丿丕乇丿 . 丕爻鬲蹖賵賳夭 趩賴丕乇 賵蹖跇诏蹖 丕氐賱蹖 亘乇丕蹖 讴賱丕賳 丿丕丿賴 卮賲乇丿賴 丕爻鬲 : 丿爻鬲乇爻蹖 丿乇 賴乇 夭賲丕賳 亘賴 噩丿蹖丿鬲乇蹖賳 丕胤賱丕毓丕鬲 貙 賮乇丕賴賲 丌賵乇丿賳 丿丕丿賴 賴丕蹖 氐丕丿賯丕賳賴 貙 丕賲讴丕賳 夭賵賲 讴乇丿賳 乇賵蹖 夭蹖乇 賲噩賲賵毓賴 賴丕蹖蹖 讴賵趩讴 丕夭 賲乇丿賲 賵 丕賲讴丕賳 倬匕蹖乇 爻丕禺鬲賳 丌夭賲賵賳 乇賵丕亘胤 毓賱蹖 趩賴丕乇 賲夭蹖鬲 讴賱丕賳 丿丕丿賴 賴爻鬲賳丿 .
丕爻丕爻 讴丕乇 賳賵蹖爻賳丿賴 亘乇 丕蹖賳 丕氐賱 丕爻鬲賵丕乇 丕爻鬲 讴賴 鬲賲丕賲蹖 丕賳爻丕賳 賴丕 鬲賲丕蹖賱 丿丕乇賳丿 丕賮讴丕乇 卮乇賲 丌賵乇禺賵丿 賲孬賱丕 丿乇 賲賵乇丿 賳跇丕丿倬乇爻鬲蹖 蹖丕 鬲亘毓蹖囟 噩賳爻蹖鬲蹖 賵 蹖丕 诏乇丕蹖卮丕鬲 毓噩蹖亘 噩賳爻蹖 卮丕賳 乇丕 丕夭 丿蹖诏乇丕賳 倬賳賴丕賳 讴賳賳丿 貙 丕賲丕 賴賳诏丕賲 丕爻鬲賮丕丿賴 丕夭 诏賵诏賱 賵 蹖丕 爻丕蹖鬲 倬賵乇賳 賴丕亘 讴賴 賴賵蹖鬲 賮乇丿 乇丕 丨賮馗 賲蹖 讴賳丿 丕賮乇丕丿 氐丕丿賯丕賳賴 亘賴 丌賳 趩賴 丿乇 賮讴乇卮丕賳 丕爻鬲 賵 蹖丕 丌賳趩賴 丿賵爻鬲 丿丕乇賳丿 丕賳噩丕賲 丿賴賳丿 丕毓鬲乇丕賮 賲蹖 讴賳賳丿 . 亘賳丕亘乇丕蹖賳 馗丕賴乇 賲丕 趩賴乇賴 丕蹖爻鬲 讴賴 丿乇 噩丕賲毓賴 蹖丕 丿乇 卮亘讴賴 賴丕蹖 丕噩鬲賲丕毓蹖 讴賴 賴賵蹖鬲 賲丕 丌卮讴丕乇 丕爻鬲 賲丕賳賳丿 丕蹖賳爻鬲丕诏乇丕賲 蹖丕 賮蹖爻 亘賵讴 賵 丨鬲蹖 诏賵丿乇蹖丿夭 丕夭 禺賵丿 賳卮丕賳 賲蹖 丿賴蹖賲 賵 亘丕胤賳 賲丕 丌賳 趩蹖夭蹖 丕蹖爻鬲 讴賴 丿乇 诏賵诏賱 蹖丕 爻丕蹖鬲 倬賵乇賳 賴丕亘 亘賴 丿賳亘丕賱 丌賳 賴爻鬲蹖賲 . 丿乇 丨賯蹖賯鬲 丿乇 丕蹖賳 爻丕蹖鬲 賴丕 賲丕 趩賴乇賴 賮乇賴賳诏蹖 貙 卮丕丿 賵 賳禺亘賴 賲丕賳 乇丕 賳卮丕賳 賲蹖 丿賴蹖賲 賳賴 禺賵丿 賵丕賯毓蹖 賲丕賳 乇丕 . 丕爻鬲蹖賵賳夭 賳卮丕賳 賲蹖 丿賴丿 讴賴 讴賱賲丕鬲 貙 讴賱蹖讴 賴丕 貙 賱蹖賳讴 賴丕 賵 丕夭 賴賲賴 賲賴賲鬲乇 噩爻鬲 賵 噩賵賴丕 賴爻鬲賳丿 讴賴 丿丕丿賴 賴丕 乇丕 鬲卮讴蹖賱 賲蹖 丿賴賳丿 . 亘賴 讴賲讴 丕蹖賳 丿丕丿賴 賴丕蹖 噩丿蹖丿 賲蹖 鬲賵丕賳 賵丕賯毓蹖鬲 倬賳賴丕賳 丿乇賵睾 賴丕蹖 賲乇丿賲 乇丕 丿蹖丿 . 賳賵蹖爻賳丿賴 丕蹖賳 丿丕丿賴 賴丕 乇丕 丕讴爻蹖乇 丨賯蹖賯鬲 丿蹖噩蹖鬲丕賱 賲蹖 賳丕賲丿 . 丕讴爻蹖乇 丨賯蹖賯鬲 賳卮丕賳 賲蹖 丿賴丿 讴賴 賲乇丿賲 毓賱丕賯賴 亘爻蹖丕乇 夭蹖丕丿蹖 亘乇丕蹖 丿丕賵乇蹖 丿蹖诏乇丕賳 亘乇 丕爻丕爻 馗丕賴乇卮丕賳 丿丕乇賳丿 貙 丕賳亘賵賴蹖 賲乇丿 賵 夭賳 诏乇丕蹖卮賴丕蹖 賴賲 噩賳爻 禺賵丕賴丕賳賴 蹖丕 鬲賲丕蹖賱丕鬲 噩賳爻蹖 賮丕賳鬲夭蹖 丿丕乇賳丿 貙 毓丿丕賵鬲蹖 賮乇丕诏蹖乇 亘丕 丌賲乇蹖讴丕蹖蹖 賴丕蹖 丌賮乇蹖賯丕蹖蹖 鬲亘丕乇 賵噩賵丿 丿丕乇丿 賵 讴賵丿讴 丌夭丕乇蹖 倬賳賴丕賳 賵 丕爻賱丕賲 賴乇丕爻蹖 禺卮賵賳鬲 丌賲蹖夭 賴賲 亘爻蹖丕乇 賮乇丕賵丕賳 丕爻鬲 .
丕讴爻蹖乇 丨賯蹖賯鬲 丿蹖噩蹖鬲丕賱 賳卮丕賳 賲蹖 丿賴丿 讴賴 賲丕 丿乇 丕賮讴丕乇 禺賵丿 鬲賳賴丕 賳蹖爻鬲蹖賲 賵 丕夭 丌賳 賲賴賲鬲乇 賲賲讴賳 丕爻鬲 亘鬲賵丕賳丿 亘賴 丕賳爻丕賳 丿乇 噩賴鬲 蹖丕賮鬲賳 乇丕賴 丨賱 賴丕蹖蹖 亘乇丕蹖 讴丕爻鬲賳 丕夭 乇賮鬲丕乇賴丕蹖 賳賮乇鬲 丕賳诏蹖夭卮 讴賲讴 讴賳丿 .
爻鬲 丕爻鬲蹖賵賳夭诏乇趩賴 讴賱丕賳 丿丕丿賴 賴丕 乇丕 賴賲丕賳賳丿 丕賳賯賱丕亘蹖 亘夭乇诏 賲蹖 丿丕賳丿 丕賲丕 賴卮丿丕乇賲蹖 丿賴丿 讴賴 趩賳丿丕賳 丕亘夭丕乇 賯丕胤毓蹖 賴賲 賳蹖爻鬲 . 讴賱丕賳 丿丕丿賴 賴丕 賲丕 乇丕 丕夭 丿蹖诏乇 乇賵卮 賴丕蹖蹖 讴賴 丕賳爻丕賳 賴丕 丕夭 乇賵夭诏丕乇丕賳 诏匕卮鬲賴 亘乇丕蹖 賮賴賲 噩賴丕賳 讴卮賮 讴乇丿賴 賵 亘賴亘賵丿 亘禺卮蹖丿賴 丕賳丿 亘蹖 賳蹖丕夭賲丕賳 賳賲蹖 讴賳丿 . 丕蹖賳 丕亘夭丕乇賴丕 賴乇 丿賵 賲讴賲賱 賴賲 賴爻鬲賳丿 .
丿乇 倬丕蹖丕賳 賳賵蹖爻賳丿賴 讴賱丕賳 丿丕丿賴 乇丕 賳爻禺賴 賯乇賳 亘蹖爻鬲 賵 蹖讴賲蹖 丕蹖賳 囟乇亘 丕賱賲孬賱 賲蹖 丿丕賳丿 : 賴蹖趩 賵賯鬲 丿乇賵賳鬲 乇丕 亘丕 亘蹖乇賵賳 丿蹖诏乇丕賳 賲賯丕蹖爻賴 賳讴賳 . 丕賵 丕賱亘鬲賴 賳爻禺賴 噩丿蹖丿蹖 丕夭 丕蹖賳 夭亘丕賳夭丿 爻丕禺鬲賴 : 賴蹖趩 賵賯鬲 噩爻鬲 賵 噩賵賴丕蹖 诏賵诏賱 禺賵丿鬲 乇丕 亘丕 倬爻鬲 賴丕蹖 卮亘讴賴 賴丕蹖 丕噩鬲賲丕毓蹖 丿蹖诏乇丕賳 賲賯丕蹖爻賴 賳讴賳 .
賳賵蹖爻賳丿賴 乇賵卮賴丕蹖蹖 賲丕賳賳丿 胤乇丕丨蹖 倬乇爻卮 賳丕賲賴 蹖丕 爻賵丕賱 讴乇丿賳 賲爻鬲賯蹖賲 丕夭 賲乇丿賲 乇丕 賳丕讴丕乇賲丿 賵 賲賳爻賵禺 賲蹖 丿丕賳丿 貙 丕賵 亘賴 噩丕蹖 丕蹖賳 乇賵卮 丕爻鬲賮丕丿賴 丕夭 讴賱丕賳 丿丕丿賴 蹖丕 亘蹖诏 丿蹖鬲丕 乇丕 倬蹖卮賳賴丕丿 賲蹖 讴賳丿 讴賴 賴乇 賱丨馗賴 亘乇 丨噩賲 丌賳 丕賮夭賵丿賴 賲蹖 卮賵丿 . 亘賴 丕蹖賳 诏賵賳賴 丿丕賳卮賲賳丿丕賳 毓賱賵賲 丕噩鬲賲丕毓蹖 亘賴 丕賯蹖丕賳賵爻蹖 丕夭 丕胤賱丕毓丕鬲 丿爻鬲 倬蹖丿丕 讴乇丿賴 丕賳丿 讴賴 鬲賯乇蹖亘丕 鬲賲丕賲蹖 賳丿丕乇丿 . 丕爻鬲蹖賵賳夭 趩賴丕乇 賵蹖跇诏蹖 丕氐賱蹖 亘乇丕蹖 讴賱丕賳 丿丕丿賴 卮賲乇丿賴 丕爻鬲 : 丿爻鬲乇爻蹖 丿乇 賴乇 夭賲丕賳 亘賴 噩丿蹖丿鬲乇蹖賳 丕胤賱丕毓丕鬲 貙 賮乇丕賴賲 丌賵乇丿賳 丿丕丿賴 賴丕蹖 氐丕丿賯丕賳賴 貙 丕賲讴丕賳 夭賵賲 讴乇丿賳 乇賵蹖 夭蹖乇 賲噩賲賵毓賴 賴丕蹖蹖 讴賵趩讴 丕夭 賲乇丿賲 賵 丕賲讴丕賳 倬匕蹖乇 爻丕禺鬲賳 丌夭賲賵賳 乇賵丕亘胤 毓賱蹖 趩賴丕乇 賲夭蹖鬲 讴賱丕賳 丿丕丿賴 賴爻鬲賳丿 .
丕爻丕爻 讴丕乇 賳賵蹖爻賳丿賴 亘乇 丕蹖賳 丕氐賱 丕爻鬲賵丕乇 丕爻鬲 讴賴 鬲賲丕賲蹖 丕賳爻丕賳 賴丕 鬲賲丕蹖賱 丿丕乇賳丿 丕賮讴丕乇 卮乇賲 丌賵乇禺賵丿 賲孬賱丕 丿乇 賲賵乇丿 賳跇丕丿倬乇爻鬲蹖 蹖丕 鬲亘毓蹖囟 噩賳爻蹖鬲蹖 賵 蹖丕 诏乇丕蹖卮丕鬲 毓噩蹖亘 噩賳爻蹖 卮丕賳 乇丕 丕夭 丿蹖诏乇丕賳 倬賳賴丕賳 讴賳賳丿 貙 丕賲丕 賴賳诏丕賲 丕爻鬲賮丕丿賴 丕夭 诏賵诏賱 賵 蹖丕 爻丕蹖鬲 倬賵乇賳 賴丕亘 讴賴 賴賵蹖鬲 賮乇丿 乇丕 丨賮馗 賲蹖 讴賳丿 丕賮乇丕丿 氐丕丿賯丕賳賴 亘賴 丌賳 趩賴 丿乇 賮讴乇卮丕賳 丕爻鬲 賵 蹖丕 丌賳趩賴 丿賵爻鬲 丿丕乇賳丿 丕賳噩丕賲 丿賴賳丿 丕毓鬲乇丕賮 賲蹖 讴賳賳丿 . 亘賳丕亘乇丕蹖賳 馗丕賴乇 賲丕 趩賴乇賴 丕蹖爻鬲 讴賴 丿乇 噩丕賲毓賴 蹖丕 丿乇 卮亘讴賴 賴丕蹖 丕噩鬲賲丕毓蹖 讴賴 賴賵蹖鬲 賲丕 丌卮讴丕乇 丕爻鬲 賲丕賳賳丿 丕蹖賳爻鬲丕诏乇丕賲 蹖丕 賮蹖爻 亘賵讴 賵 丨鬲蹖 诏賵丿乇蹖丿夭 丕夭 禺賵丿 賳卮丕賳 賲蹖 丿賴蹖賲 賵 亘丕胤賳 賲丕 丌賳 趩蹖夭蹖 丕蹖爻鬲 讴賴 丿乇 诏賵诏賱 蹖丕 爻丕蹖鬲 倬賵乇賳 賴丕亘 亘賴 丿賳亘丕賱 丌賳 賴爻鬲蹖賲 . 丿乇 丨賯蹖賯鬲 丿乇 丕蹖賳 爻丕蹖鬲 賴丕 賲丕 趩賴乇賴 賮乇賴賳诏蹖 貙 卮丕丿 賵 賳禺亘賴 賲丕賳 乇丕 賳卮丕賳 賲蹖 丿賴蹖賲 賳賴 禺賵丿 賵丕賯毓蹖 賲丕賳 乇丕 . 丕爻鬲蹖賵賳夭 賳卮丕賳 賲蹖 丿賴丿 讴賴 讴賱賲丕鬲 貙 讴賱蹖讴 賴丕 貙 賱蹖賳讴 賴丕 賵 丕夭 賴賲賴 賲賴賲鬲乇 噩爻鬲 賵 噩賵賴丕 賴爻鬲賳丿 讴賴 丿丕丿賴 賴丕 乇丕 鬲卮讴蹖賱 賲蹖 丿賴賳丿 . 亘賴 讴賲讴 丕蹖賳 丿丕丿賴 賴丕蹖 噩丿蹖丿 賲蹖 鬲賵丕賳 賵丕賯毓蹖鬲 倬賳賴丕賳 丿乇賵睾 賴丕蹖 賲乇丿賲 乇丕 丿蹖丿 . 賳賵蹖爻賳丿賴 丕蹖賳 丿丕丿賴 賴丕 乇丕 丕讴爻蹖乇 丨賯蹖賯鬲 丿蹖噩蹖鬲丕賱 賲蹖 賳丕賲丿 . 丕讴爻蹖乇 丨賯蹖賯鬲 賳卮丕賳 賲蹖 丿賴丿 讴賴 賲乇丿賲 毓賱丕賯賴 亘爻蹖丕乇 夭蹖丕丿蹖 亘乇丕蹖 丿丕賵乇蹖 丿蹖诏乇丕賳 亘乇 丕爻丕爻 馗丕賴乇卮丕賳 丿丕乇賳丿 貙 丕賳亘賵賴蹖 賲乇丿 賵 夭賳 诏乇丕蹖卮賴丕蹖 賴賲 噩賳爻 禺賵丕賴丕賳賴 蹖丕 鬲賲丕蹖賱丕鬲 噩賳爻蹖 賮丕賳鬲夭蹖 丿丕乇賳丿 貙 毓丿丕賵鬲蹖 賮乇丕诏蹖乇 亘丕 丌賲乇蹖讴丕蹖蹖 賴丕蹖 丌賮乇蹖賯丕蹖蹖 鬲亘丕乇 賵噩賵丿 丿丕乇丿 賵 讴賵丿讴 丌夭丕乇蹖 倬賳賴丕賳 賵 丕爻賱丕賲 賴乇丕爻蹖 禺卮賵賳鬲 丌賲蹖夭 賴賲 亘爻蹖丕乇 賮乇丕賵丕賳 丕爻鬲 .
丕讴爻蹖乇 丨賯蹖賯鬲 丿蹖噩蹖鬲丕賱 賳卮丕賳 賲蹖 丿賴丿 讴賴 賲丕 丿乇 丕賮讴丕乇 禺賵丿 鬲賳賴丕 賳蹖爻鬲蹖賲 賵 丕夭 丌賳 賲賴賲鬲乇 賲賲讴賳 丕爻鬲 亘鬲賵丕賳丿 亘賴 丕賳爻丕賳 丿乇 噩賴鬲 蹖丕賮鬲賳 乇丕賴 丨賱 賴丕蹖蹖 亘乇丕蹖 讴丕爻鬲賳 丕夭 乇賮鬲丕乇賴丕蹖 賳賮乇鬲 丕賳诏蹖夭卮 讴賲讴 讴賳丿 .
爻鬲 丕爻鬲蹖賵賳夭诏乇趩賴 讴賱丕賳 丿丕丿賴 賴丕 乇丕 賴賲丕賳賳丿 丕賳賯賱丕亘蹖 亘夭乇诏 賲蹖 丿丕賳丿 丕賲丕 賴卮丿丕乇賲蹖 丿賴丿 讴賴 趩賳丿丕賳 丕亘夭丕乇 賯丕胤毓蹖 賴賲 賳蹖爻鬲 . 讴賱丕賳 丿丕丿賴 賴丕 賲丕 乇丕 丕夭 丿蹖诏乇 乇賵卮 賴丕蹖蹖 讴賴 丕賳爻丕賳 賴丕 丕夭 乇賵夭诏丕乇丕賳 诏匕卮鬲賴 亘乇丕蹖 賮賴賲 噩賴丕賳 讴卮賮 讴乇丿賴 賵 亘賴亘賵丿 亘禺卮蹖丿賴 丕賳丿 亘蹖 賳蹖丕夭賲丕賳 賳賲蹖 讴賳丿 . 丕蹖賳 丕亘夭丕乇賴丕 賴乇 丿賵 賲讴賲賱 賴賲 賴爻鬲賳丿 .
丿乇 倬丕蹖丕賳 賳賵蹖爻賳丿賴 讴賱丕賳 丿丕丿賴 乇丕 賳爻禺賴 賯乇賳 亘蹖爻鬲 賵 蹖讴賲蹖 丕蹖賳 囟乇亘 丕賱賲孬賱 賲蹖 丿丕賳丿 : 賴蹖趩 賵賯鬲 丿乇賵賳鬲 乇丕 亘丕 亘蹖乇賵賳 丿蹖诏乇丕賳 賲賯丕蹖爻賴 賳讴賳 . 丕賵 丕賱亘鬲賴 賳爻禺賴 噩丿蹖丿蹖 丕夭 丕蹖賳 夭亘丕賳夭丿 爻丕禺鬲賴 : 賴蹖趩 賵賯鬲 噩爻鬲 賵 噩賵賴丕蹖 诏賵诏賱 禺賵丿鬲 乇丕 亘丕 倬爻鬲 賴丕蹖 卮亘讴賴 賴丕蹖 丕噩鬲賲丕毓蹖 丿蹖诏乇丕賳 賲賯丕蹖爻賴 賳讴賳 .
February 4, 2023
3.5
讴鬲丕亘 倬乇 丕夭 丌賲丕乇 噩丕賱亘 鬲賵噩賴賴 賵 丿乇 賵丕賯毓 亘丕 亘乇乇爻蹖 亘丕夭丿蹖丿賴丕蹖 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 賵 爻乇趩鈥屬囏й� 诏賵诏賱貙 禺蹖賱蹖 丿賯蹖賯鈥屫� 丕夭 禺蹖賱蹖 賳馗乇爻賳噩蹖鈥屬囏� 賵 賲胤丕賱毓丕鬲 噩丕賲毓賴鈥屫促嗀ж驰� 賳卮賵賳 賲蹖鈥屫� 讴賴 賲乇丿賲 亘乇睾賲 丕賵賳趩賴 讴賴 賲蹖诏賳貙 趩蹖夭 丿蹖诏賴鈥屫й� 丿乇 爻乇 丿丕乇賳.
讴賱 丨乇賮 讴鬲丕亘 乇賵 丿乇 蹖讴 讴賱丕賲 賲蹖鈥屫促� 丕蹖賳胤賵乇 禺賱丕氐賴 讴乇丿 讴賴 賲乇丿賲 亘賴 賴夭丕乇丕賳 丿賱蹖賱 賲禺鬲賱賮 丕丨鬲賲丕賱丕 丿乇 賳馗乇爻賳噩蹖鈥屬囏� 丿乇賵睾 禺賵丕賴賳丿 诏賮鬲 蹖丕 丨丿丕賯賱 丨賯蹖賯鬲 乇賵 亘賴 胤賵乇 讴丕賲賱 賳禺賵丕賴賳丿 诏賮鬲. 鬲賳賴丕 噩丕蹖蹖 讴賴 賲乇丿賲 倬蹖卮 丕賵賳 亘乇丕丨鬲蹖 丕毓鬲乇丕賮 賲蹖鈥屭┵嗁� 蹖丕 賳丕丿丕賳爻鬲賴 禺賵丿 賵丕賯毓蹖鈥屫促堎� 乇賵 亘蹖鈥屬矩必� 丌卮讴丕乇 賲蹖鈥屭┵嗁� 丿乇 倬蹖卮 趩卮賲 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌� 賴爻鬲. 亘禺卮 毓賲丿賴 讴鬲丕亘 亘賴 賲氐丕丿蹖賯 賲禺鬲賱賮 丕蹖賳 賯囟蹖賴 賵 丕爻鬲賮丕丿賴鈥屬囏й� 鬲噩丕乇蹖 蹖丕 噩丕賲毓賴 卮賳丕禺鬲蹖 亘丕賱賯賵賴鈥� 丕賵賳 賲蹖鈥屬矩必ж操� 讴賴 丕賳氐丕賮丕 賴賲 噩丕賱亘 鬲賵噩賴 亘賵丿.
亘丕 丕蹖賳 賴賲賴 亘賳馗乇 禺賵丿 賲賳貙 丕诏乇趩賴 賳賵蹖爻賳丿賴 乇賵蹖 丕賳賯賱丕亘蹖 亘賵丿賳 丕蹖賳 乇賵卮 賲丕賳賵乇 賲蹖鈥屫囏� 賵賱蹖 賴賲趩賳丕賳 賱丕夭賲賴 鬲賵噩賴 讴賳蹖賲 讴賴 丿乇 禺蹖賱蹖 賲賵丕乇丿貙 賲蹖鈥屫促� 鬲賮爻蹖乇賴丕蹖 賲鬲賮丕賵鬲蹖 丕夭 賳鬲丕蹖噩 丿丕卮鬲 賵 丕賵賳賯丿乇蹖 讴賴 賳賵蹖爻賳丿賴 丿賱卮 賲蹖鈥屫堌ж� 賳卮賵賳 亘丿賴貙 丌賲丕乇 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌堌� 丨賯蹖賯鬲 乇賵 卮爻鬲賴 賵 乇賮鬲賴 鬲丨賵蹖賱 賲丕 賳賲蹖鈥屫�. 賲孬丕賱 賲蹖鈥屫操嗁�: 丕
賲孬賱丕 丕蹖賳讴賴 丿乇 亘蹖賳 倬乇 亘丕夭丿蹖丿鬲乇蹖賳 賵蹖丿卅賵賴丕貙 賴蹖趩 賵蹖丿卅賵 倬賵乇賳蹖 賵噩賵丿 賳丿丕乇賴貙 賱夭賵賲丕 趩蹖夭 亘禺氐賵氐蹖 乇賵 賳卮賵賳 賳賲蹖鈥屫�. 賯丕毓丿鬲丕 卮賲丕 賵賯鬲蹖 賵蹖丿卅賵 "诏丕賳诏賳丕賲 丕爻鬲丕蹖賱" 乇賵 賲蹖鈥屫ㄛ屬嗃� 丿賱丕蹖賱 夭蹖丕丿蹖 丿丕乇蹖 讴賴 丕賵賳 乇賵 亘丕夭 賳卮乇 讴賳蹖 亘乇丕蹖 丿賵爻鬲丕鬲 蹖丕 禺賵賳賵丕丿鬲 賵賱蹖 賵賯鬲蹖 賵蹖丿卅賵 倬賵乇賳 賲蹖鈥屫ㄛ屬嗃� 丕蹖賳讴丕乇 乇賵 賴乇诏夭 賳禺賵丕賴蹖 讴乇丿. 賴賲蹖賳 乇賵蹖 鬲毓丿丕丿 亘丕夭丿蹖丿賴丕 禺蹖賱蹖 丕孬乇 賲蹖诏匕丕乇賴.
丿乇 賵丕賯毓 诏丕賴蹖 亘賳馗乇賲 賲蹖賵賲丿 讴賴 鬲丕讴蹖丿 亘蹖卮 丕夭 丨丿 乇賵蹖 丌賲丕乇 賵 丕乇賯丕賲 亘丕毓孬 卮丿賴 賳賵蹖爻賳丿賴 賳鬲蹖噩賴鈥屭屫臂屸€屬囏й� 卮鬲丕亘鈥屫藏団€屫й� 丕賳噩丕賲 亘丿賴. 賴賲蹖賳 蹖讴賲 鬲賵蹖 匕賵賯賲 夭丿. 賵賱蹖 丕賵賳 賲胤丕賱亘卮 讴賴 乇丕噩毓 亘賴 丌夭賲賵賳鈥屬囏й� 丕賱賮-亘 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌� 賵 賮蹖爻鈥屫ㄙ堏� 乇賵蹖 讴丕乇亘乇丕賳 亘賵丿 禺蹖賱蹖 亘乇丕賲 噩丕賱亘 亘賵丿.
爻乇 噩賲毓 丨鬲賲丕 丕乇夭卮 禺賵賳丿賳 丿丕乇賴 賵賱蹖 亘丕蹖丿 亘丿賵賳蹖丿 讴賴 讴鬲丕亘 賴乇诏夭 丕夭 爻胤丨 丕胤賱丕毓丕鬲 毓賲賵賲蹖 賮乇丕鬲乇 賳賲蹖鈥屫辟� 賵 趩蹖夭蹖 蹖丕丿鬲賵賳 賳賲蹖鈥屫�.
讴鬲丕亘 倬乇 丕夭 丌賲丕乇 噩丕賱亘 鬲賵噩賴賴 賵 丿乇 賵丕賯毓 亘丕 亘乇乇爻蹖 亘丕夭丿蹖丿賴丕蹖 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 賵 爻乇趩鈥屬囏й� 诏賵诏賱貙 禺蹖賱蹖 丿賯蹖賯鈥屫� 丕夭 禺蹖賱蹖 賳馗乇爻賳噩蹖鈥屬囏� 賵 賲胤丕賱毓丕鬲 噩丕賲毓賴鈥屫促嗀ж驰� 賳卮賵賳 賲蹖鈥屫� 讴賴 賲乇丿賲 亘乇睾賲 丕賵賳趩賴 讴賴 賲蹖诏賳貙 趩蹖夭 丿蹖诏賴鈥屫й� 丿乇 爻乇 丿丕乇賳.
讴賱 丨乇賮 讴鬲丕亘 乇賵 丿乇 蹖讴 讴賱丕賲 賲蹖鈥屫促� 丕蹖賳胤賵乇 禺賱丕氐賴 讴乇丿 讴賴 賲乇丿賲 亘賴 賴夭丕乇丕賳 丿賱蹖賱 賲禺鬲賱賮 丕丨鬲賲丕賱丕 丿乇 賳馗乇爻賳噩蹖鈥屬囏� 丿乇賵睾 禺賵丕賴賳丿 诏賮鬲 蹖丕 丨丿丕賯賱 丨賯蹖賯鬲 乇賵 亘賴 胤賵乇 讴丕賲賱 賳禺賵丕賴賳丿 诏賮鬲. 鬲賳賴丕 噩丕蹖蹖 讴賴 賲乇丿賲 倬蹖卮 丕賵賳 亘乇丕丨鬲蹖 丕毓鬲乇丕賮 賲蹖鈥屭┵嗁� 蹖丕 賳丕丿丕賳爻鬲賴 禺賵丿 賵丕賯毓蹖鈥屫促堎� 乇賵 亘蹖鈥屬矩必� 丌卮讴丕乇 賲蹖鈥屭┵嗁� 丿乇 倬蹖卮 趩卮賲 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌� 賴爻鬲. 亘禺卮 毓賲丿賴 讴鬲丕亘 亘賴 賲氐丕丿蹖賯 賲禺鬲賱賮 丕蹖賳 賯囟蹖賴 賵 丕爻鬲賮丕丿賴鈥屬囏й� 鬲噩丕乇蹖 蹖丕 噩丕賲毓賴 卮賳丕禺鬲蹖 亘丕賱賯賵賴鈥� 丕賵賳 賲蹖鈥屬矩必ж操� 讴賴 丕賳氐丕賮丕 賴賲 噩丕賱亘 鬲賵噩賴 亘賵丿.
亘丕 丕蹖賳 賴賲賴 亘賳馗乇 禺賵丿 賲賳貙 丕诏乇趩賴 賳賵蹖爻賳丿賴 乇賵蹖 丕賳賯賱丕亘蹖 亘賵丿賳 丕蹖賳 乇賵卮 賲丕賳賵乇 賲蹖鈥屫囏� 賵賱蹖 賴賲趩賳丕賳 賱丕夭賲賴 鬲賵噩賴 讴賳蹖賲 讴賴 丿乇 禺蹖賱蹖 賲賵丕乇丿貙 賲蹖鈥屫促� 鬲賮爻蹖乇賴丕蹖 賲鬲賮丕賵鬲蹖 丕夭 賳鬲丕蹖噩 丿丕卮鬲 賵 丕賵賳賯丿乇蹖 讴賴 賳賵蹖爻賳丿賴 丿賱卮 賲蹖鈥屫堌ж� 賳卮賵賳 亘丿賴貙 丌賲丕乇 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌堌� 丨賯蹖賯鬲 乇賵 卮爻鬲賴 賵 乇賮鬲賴 鬲丨賵蹖賱 賲丕 賳賲蹖鈥屫�. 賲孬丕賱 賲蹖鈥屫操嗁�: 丕
賲孬賱丕 丕蹖賳讴賴 丿乇 亘蹖賳 倬乇 亘丕夭丿蹖丿鬲乇蹖賳 賵蹖丿卅賵賴丕貙 賴蹖趩 賵蹖丿卅賵 倬賵乇賳蹖 賵噩賵丿 賳丿丕乇賴貙 賱夭賵賲丕 趩蹖夭 亘禺氐賵氐蹖 乇賵 賳卮賵賳 賳賲蹖鈥屫�. 賯丕毓丿鬲丕 卮賲丕 賵賯鬲蹖 賵蹖丿卅賵 "诏丕賳诏賳丕賲 丕爻鬲丕蹖賱" 乇賵 賲蹖鈥屫ㄛ屬嗃� 丿賱丕蹖賱 夭蹖丕丿蹖 丿丕乇蹖 讴賴 丕賵賳 乇賵 亘丕夭 賳卮乇 讴賳蹖 亘乇丕蹖 丿賵爻鬲丕鬲 蹖丕 禺賵賳賵丕丿鬲 賵賱蹖 賵賯鬲蹖 賵蹖丿卅賵 倬賵乇賳 賲蹖鈥屫ㄛ屬嗃� 丕蹖賳讴丕乇 乇賵 賴乇诏夭 賳禺賵丕賴蹖 讴乇丿. 賴賲蹖賳 乇賵蹖 鬲毓丿丕丿 亘丕夭丿蹖丿賴丕 禺蹖賱蹖 丕孬乇 賲蹖诏匕丕乇賴.
丿乇 賵丕賯毓 诏丕賴蹖 亘賳馗乇賲 賲蹖賵賲丿 讴賴 鬲丕讴蹖丿 亘蹖卮 丕夭 丨丿 乇賵蹖 丌賲丕乇 賵 丕乇賯丕賲 亘丕毓孬 卮丿賴 賳賵蹖爻賳丿賴 賳鬲蹖噩賴鈥屭屫臂屸€屬囏й� 卮鬲丕亘鈥屫藏団€屫й� 丕賳噩丕賲 亘丿賴. 賴賲蹖賳 蹖讴賲 鬲賵蹖 匕賵賯賲 夭丿. 賵賱蹖 丕賵賳 賲胤丕賱亘卮 讴賴 乇丕噩毓 亘賴 丌夭賲賵賳鈥屬囏й� 丕賱賮-亘 賲賵鬲賵乇賴丕蹖 噩爻鬲鈥屬堌� 賵 賮蹖爻鈥屫ㄙ堏� 乇賵蹖 讴丕乇亘乇丕賳 亘賵丿 禺蹖賱蹖 亘乇丕賲 噩丕賱亘 亘賵丿.
爻乇 噩賲毓 丨鬲賲丕 丕乇夭卮 禺賵賳丿賳 丿丕乇賴 賵賱蹖 亘丕蹖丿 亘丿賵賳蹖丿 讴賴 讴鬲丕亘 賴乇诏夭 丕夭 爻胤丨 丕胤賱丕毓丕鬲 毓賲賵賲蹖 賮乇丕鬲乇 賳賲蹖鈥屫辟� 賵 趩蹖夭蹖 蹖丕丿鬲賵賳 賳賲蹖鈥屫�.
October 26, 2019
Everybody Lies: Big Data, New Data, and What the Internet Reveals About Who We Really Are by Seth Stephens-Davidowitz takes us into the world of social sciences via the internet. I might have found the book version a bit on the boring side but I enjoyed the audiobook. Big Data can answer any and all of our questions. But will the answer be what we want to hear. And do we need to know about all aspects of the world we live in. The book is similar to some of Malcolm Gladwell's work but it is not Malcolm Gladwell. However, you will be informed, you will learn about our world and you will sometimes be amused.
July 23, 2017
No practicing analyst or social scientist will find anything of value in this book. It verges on being dangerously deceptive, filled with logical fallacies and half baked reasoning for it's conclusions. The book claims to be finding truth in an uncertain world, but actually is just adding to the noise.
November 27, 2017
I wanted to like this book. It's an interesting topic. But I found the methodology extremely sloppy. Or maybe the author just omitted some key facts. He was clearly determined to prove that racism caused the election of Donald Trump. But it's disconcerting to read the conclusion BEFORE the data analysis itself. On one hand, he says that Obama easily won two terms, DESPITE racism. Then he quickly says that Trump won the 2016 election BECAUSE of racism. So which is it? Is racism so widespread that it caused both candidates to win, the black man despite it, the white man because of it? It makes no sense. Nor was I terribly convinced that Google searches for the word n-gger are actually clearcut reflections of a person who would never vote for a black president but always vote for Trump. That's a pretty big leap of logic. As is his notion that black people would spell it "n-gga" therefore all these searches are by white racists. Also, is it a real absolute that racists would never vote for a black president? After all, you could vote for Obama because you feel he's the best of two options, yet still be a flaming racist. Likewise, if you search Google for racist jokes, does that actually prove that you are treating minorities unfairly? It may sound like a reasonable conjecture but this is data science, not an op-ed column. There should be a more decisive connection before making a grand sweeping statement that Trump won due to racists but Obama won despite racists.
The author is even sloppier in the section on searches of a pornographic nature. He refers to a data set from a porn site called PornHub. He has to assume that anyone who registers on that site and states "I am male" or "I am female" is absolutely telling the truth. But how do we know that? Are we sure that men never pretend to be women to chat with others, exchange messages, or share videos on porn sites? I'm not convinced.
As was widely reported, 25% of searches by (alleged) women on porn sites are for rather violent porn. I don't mean a little spanking, but hardcore search terms including words like "brutal" and "crying" and so forth. 20% of the (alleged) women's searches are for lesbian porn. But the author is quick to point out: this is sexual fantasy! It's not real life! Those women aren't actual lesbians, nor do they want to engage in violent sex.
But when it comes to men's searches, he regards those as literal fact. If men search for gay porn, it's because they're gay, maybe closeted, but definitely gay. Why does he insist this is true for men, but not for women?
The same sloppy reasoning is applied to various other search terms. The fact that "boyfriend won't have sex" is far more common than "girlfriend won't have sex" is the foundation for his notion that men are more likely to refuse sex to their partners than vice versa. But how do we know that's actually true? What about the notion that women are more likely to SEARCH for a solution to this problem online?
The fact is that we know absolutely nothing about the people performing these searches - whether they are male or female, racist or fair-minded, gay or straight. So making assumptions about their motivations based solely on search terms is just poor data science. Maybe there's some essential research that the author omitted. But it looks like pure speculation based on search terms, which is not what I would expect of an author who claims to be a data scientist.
Stick with Freakonomics if this topic interests you.
The author is even sloppier in the section on searches of a pornographic nature. He refers to a data set from a porn site called PornHub. He has to assume that anyone who registers on that site and states "I am male" or "I am female" is absolutely telling the truth. But how do we know that? Are we sure that men never pretend to be women to chat with others, exchange messages, or share videos on porn sites? I'm not convinced.
As was widely reported, 25% of searches by (alleged) women on porn sites are for rather violent porn. I don't mean a little spanking, but hardcore search terms including words like "brutal" and "crying" and so forth. 20% of the (alleged) women's searches are for lesbian porn. But the author is quick to point out: this is sexual fantasy! It's not real life! Those women aren't actual lesbians, nor do they want to engage in violent sex.
But when it comes to men's searches, he regards those as literal fact. If men search for gay porn, it's because they're gay, maybe closeted, but definitely gay. Why does he insist this is true for men, but not for women?
The same sloppy reasoning is applied to various other search terms. The fact that "boyfriend won't have sex" is far more common than "girlfriend won't have sex" is the foundation for his notion that men are more likely to refuse sex to their partners than vice versa. But how do we know that's actually true? What about the notion that women are more likely to SEARCH for a solution to this problem online?
The fact is that we know absolutely nothing about the people performing these searches - whether they are male or female, racist or fair-minded, gay or straight. So making assumptions about their motivations based solely on search terms is just poor data science. Maybe there's some essential research that the author omitted. But it looks like pure speculation based on search terms, which is not what I would expect of an author who claims to be a data scientist.
Stick with Freakonomics if this topic interests you.
October 18, 2021
I was annoyed by the author鈥檚 writing style in 鈥楨verybody Lies鈥�. I have no doubts author Seth Stephens-Davidowitz was trying to write to a large general audience, including that assumed class of American non-science reader who hates math and binge watches 鈥楰eeping Up with the Kardashians鈥�. Good for him, and maybe you, right? But I became more and more annoyed as I read. Ah, well. It is an interesting and informative read, in spite of trying too hard to be fun, imho.
What is the book about? I am glad to report it has genuine information about the science of statistics and 鈥榖ig data鈥� collecting, and how the erroneous selection of study parameters or assumptions about what is relevant data to study affects conclusions (as far as I know - I am a dunce at scientific math, despite that I passed a statistics class). The author used what seemed to me genuinely interesting new methods to formulate statistical studies, primarily using Google鈥檚 forensic tools, along with other sources.
I was shocked by what people type into Google Search (which Google compiles into anonymous data). For example, President Obama鈥檚 race appears to have truly ignited racists into coming out of their closets. Comparing survey interviews with people who state they are racist (a low percentage) with the percentage of those who Googled 鈥渘 jokes鈥� state by state turns out to show some truly hidden pockets of unexpected racism - and the total percentage of racist searches on Google was WAY higher than the racism that typical surveys show. In addition, those places who adore Trump also searched most for 鈥渘 jokes鈥�. Correlation? Idk, no one does know for the record, but I think yes.
Also of interest to me (please don鈥檛 bust my balls because of my prurient interests - and maybe there is a pun in this sentence, hehheh - read on) men really truly do Google a lot about penis sizes. Come on, fellas, give it a rest! (Yes, I am trying to be snarky since the too much 鈥榓t rest鈥� position is part of what men appear to be most anxious about!) Men prowl porn sites in humongous numbers - shocking, right? - which is good for statisticians looking for Truth about sexuality for their inputs into their mathematical equations. Based on Google porn searches, the author estimates 5% of the population is gay. (Btw, conservatives mostly use the word 鈥榟omosexual鈥� while liberals use the phrase 鈥榮ame-sex鈥�, statistically, in Google searches.)
Not to neglect what Google says about what the ladies鈥� biggest sexual worry is, all I can say is, Oh. My. God. Vagina odor. Really? Really!!
All statisticians should take note - interrogative surveys often show different results from those statistics revealed in Google searches about the percentages of who is thinking/feeling what where and when, especially in those morally-weighted or personally embarrassing areas of society. Of course, interpretation is always fraught with possible erroneous judgements whatever the source of sampling.
I have always trusted those insurance actuarial tables FAR more than political or media spins or even university data studies - so now I am adding Google statistics to my 鈥榯rusted info鈥� list. Of course, gentle reader, I know any compilations of data can be erroneously or purposely manipulated or massaged. 鈥楪arbage in, garbage out鈥� still applies...which is the case 鈥楨verybody Lies鈥� makes as well. The book seemed on top of the science, as far as I know. I am not a science-brain, but an amateur wannabe.
My one irritation with this book is all about the manner in which the information is explained. Gentle reader, my complaint is subjective as hell. Honestly, I can鈥檛 put my finger on it, though. The writer seemed to be trying to fill out his actual 200-page book to 300 pages by having personal emotional filler similar to the gaspy asides many shows use to increase the viewers鈥� emotional high about what is being discussed. Are you familiar with those TV shows that, after each commercial break, recap the entire show in the preceding minutes before the commercial break in a breathless montage manner? And they often had a shocked-gasp teaser of what will be shown before the commercial break? Anyway, I felt there was a lot of that style of emotional manipulation (and extending of the material) going on in this book, somehow. I simply did not appreciate the personal 鈥榝un鈥� filler so much. Maybe there wasn鈥檛 enough snark. I prefer snarky humor, if there is humor. Bite me. Maybe a more tightly edited book would have worked better for me to enjoy reading it. Anyway, I realize I am floundering about here. None of this may be true at all for you.
Ultimately, this is a book worthy of reading for the general reader (for the record, I definitely have a lit/history brain, so yes, I am a general science reader!) and the explanatory information about how statistical studies are done (the only math-involved college class which engaged me) and what people are really feeling and thinking (if Google searches are to be believed, and I think they are).
Included are extensive Notes and Index sections.
What is the book about? I am glad to report it has genuine information about the science of statistics and 鈥榖ig data鈥� collecting, and how the erroneous selection of study parameters or assumptions about what is relevant data to study affects conclusions (as far as I know - I am a dunce at scientific math, despite that I passed a statistics class). The author used what seemed to me genuinely interesting new methods to formulate statistical studies, primarily using Google鈥檚 forensic tools, along with other sources.
I was shocked by what people type into Google Search (which Google compiles into anonymous data). For example, President Obama鈥檚 race appears to have truly ignited racists into coming out of their closets. Comparing survey interviews with people who state they are racist (a low percentage) with the percentage of those who Googled 鈥渘 jokes鈥� state by state turns out to show some truly hidden pockets of unexpected racism - and the total percentage of racist searches on Google was WAY higher than the racism that typical surveys show. In addition, those places who adore Trump also searched most for 鈥渘 jokes鈥�. Correlation? Idk, no one does know for the record, but I think yes.
Also of interest to me (please don鈥檛 bust my balls because of my prurient interests - and maybe there is a pun in this sentence, hehheh - read on) men really truly do Google a lot about penis sizes. Come on, fellas, give it a rest! (Yes, I am trying to be snarky since the too much 鈥榓t rest鈥� position is part of what men appear to be most anxious about!) Men prowl porn sites in humongous numbers - shocking, right? - which is good for statisticians looking for Truth about sexuality for their inputs into their mathematical equations. Based on Google porn searches, the author estimates 5% of the population is gay. (Btw, conservatives mostly use the word 鈥榟omosexual鈥� while liberals use the phrase 鈥榮ame-sex鈥�, statistically, in Google searches.)
Not to neglect what Google says about what the ladies鈥� biggest sexual worry is, all I can say is, Oh. My. God. Vagina odor. Really? Really!!
All statisticians should take note - interrogative surveys often show different results from those statistics revealed in Google searches about the percentages of who is thinking/feeling what where and when, especially in those morally-weighted or personally embarrassing areas of society. Of course, interpretation is always fraught with possible erroneous judgements whatever the source of sampling.
I have always trusted those insurance actuarial tables FAR more than political or media spins or even university data studies - so now I am adding Google statistics to my 鈥榯rusted info鈥� list. Of course, gentle reader, I know any compilations of data can be erroneously or purposely manipulated or massaged. 鈥楪arbage in, garbage out鈥� still applies...which is the case 鈥楨verybody Lies鈥� makes as well. The book seemed on top of the science, as far as I know. I am not a science-brain, but an amateur wannabe.
My one irritation with this book is all about the manner in which the information is explained. Gentle reader, my complaint is subjective as hell. Honestly, I can鈥檛 put my finger on it, though. The writer seemed to be trying to fill out his actual 200-page book to 300 pages by having personal emotional filler similar to the gaspy asides many shows use to increase the viewers鈥� emotional high about what is being discussed. Are you familiar with those TV shows that, after each commercial break, recap the entire show in the preceding minutes before the commercial break in a breathless montage manner? And they often had a shocked-gasp teaser of what will be shown before the commercial break? Anyway, I felt there was a lot of that style of emotional manipulation (and extending of the material) going on in this book, somehow. I simply did not appreciate the personal 鈥榝un鈥� filler so much. Maybe there wasn鈥檛 enough snark. I prefer snarky humor, if there is humor. Bite me. Maybe a more tightly edited book would have worked better for me to enjoy reading it. Anyway, I realize I am floundering about here. None of this may be true at all for you.
Ultimately, this is a book worthy of reading for the general reader (for the record, I definitely have a lit/history brain, so yes, I am a general science reader!) and the explanatory information about how statistical studies are done (the only math-involved college class which engaged me) and what people are really feeling and thinking (if Google searches are to be believed, and I think they are).
Included are extensive Notes and Index sections.
February 4, 2021
賲賳 亘丕 丕蹖賳 讴鬲丕亘 蹖賴 夭丕賵蹖賴鈥屫й� 丿丕乇賲 讴賴 禺賵丿賲賲 賳賲丿賵賳賲 丿賵爻鬲卮 丿丕乇賲 蹖丕 賳賴.
讴鬲丕亘 賲賮蹖丿蹖 亘賵丿. 丿乇 丕蹖賳 卮讴蹖 賳蹖爻鬲. 丕賲丕 氐丕丿賯丕賳賴 賳賵蹖爻賳丿賴 讴丕乇 禺丕氐蹖 賳讴乇丿賴 亘賵丿. 蹖賴 賲賵囟賵毓蹖 讴賴 亘賴鈥屫簇� 鬲賵蹖 爻丕賱鈥屬囏й� 丕禺蹖乇 丿丕睾賴 (讴賱丕賸 賲賵囟賵毓丕鬲 賲乇亘賵胤 亘賴 鬲丨賱蹖賱 丿丕丿賴 賵 丕賯鬲氐丕丿 賵 鬲丨賱蹖賱 乇賮鬲丕乇 乇賵 讴蹖 丿賵爻鬲 賳丿丕乇賴 亘丿賵賳賴責) 乇賵 丕賳鬲禺丕亘 讴乇丿賴 賵 丿賲 丿爻鬲蹖鈥屫臂屬� 卮賵丕賴丿 乇賵 賴賲 亘賴 毓賳賵丕賳 诏賵丕賴 氐丨亘鬲鈥屬囏ж� 丌賵乇丿賴. 賲賳 賲卮讴賱蹖 亘丕 丿賲 丿爻鬲蹖 亘賵丿賳鈥屫促堎� 賳丿丕乇賲貙 丕鬲賮丕賯丕賸 亘丕毓孬 賲蹖鈥屫促� 賲禺丕胤亘 毓丕賲 賵 讴爻丕蹖蹖 讴賴 禺蹖賱蹖 禺賵卮鈥屫ㄛ屬嗁� 賴賲 蹖賴 鬲讴賵賳蹖 亘禺賵乇賳貙 丕賲丕 賲卮讴賱賲 亘丕 賳丨賵踿 鬲丨賱蹖賱 賳賵蹖爻賳丿賴鈥屫池�. 賲賳 丿賱賲 賲蹖鈥屫堌ж池� 亘賴 賳賵蹖爻賳丿賴 丕蹖賲蹖賱 亘夭賳賲 亘丕賴丕卮 氐丨亘鬲 讴賳賲 丨鬲蹖. 乇賮鬲賲 爻丕蹖鬲卮 乇賵 禺賵賳丿賲貙 賵蹖丿蹖賵賴丕 賵 爻禺賳乇丕賳蹖鈥屬囏ж� 乇賵 賳诏丕賴 讴乇丿賲貙 丨鬲蹖 趩賳丿 禺胤蹖 賴賲 賳賵卮鬲賲貙 丕賲丕 亘賴 賳馗乇賲 讴丕乇賲 亘蹖鈥屬佖й屫� 丕賵賲丿 賵 氐乇賮 賳馗乇 讴乇丿賲 趩賵賳 蹖賴 賮丕乇睾 丕賱鬲丨氐蹖賱 丿讴鬲乇丕蹖 馗丕賴乇丕賸 禺賮賳賽 丕賲乇蹖讴丕蹖蹖 賯胤毓丕賸 亘賴 噩丕蹖蹖卮 賳蹖爻鬲 賲賳賽 丕蹖乇丕賳蹖 賱蹖爻丕賳爻鈥屫堎嗀� 賵 禺購乇丿 賵 亘丿亘禺鬲 亘蹖丕賲 賳賯丿卮 讴賳賲. :)
毓賳賵丕賳 讴鬲丕亘 亘賴鬲乇蹖賳 亘禺卮卮賴貙 趩賵賳 丿乇亘丕乇踿 賳賵蹖爻賳丿賴 賴賲 氐丕丿賯賴.
賳賵蹖爻賳丿賴 丿乇賵睾 賲蹖鈥屭�. 亘丕乇賴丕 丿乇 賯爻賲鬲鈥屬囏й屰� 丕夭 讴鬲丕亘貙 丿乇 毓蹖賳 丕蹖賳讴賴 賴蹖噩丕賳鈥屫藏� 賲蹖鈥屫簇呚� 蹖賴鈥屭┵呝� 亘丿亘蹖賳 賲蹖鈥屫簇� 讴賴 毓賴貙 亘亘蹖賳貙 賲诏賴 賲丕 丕蹖賳丕 乇賵 賳賲蹖鈥屫堎嗃屬呚� 賲诏賴 賲賱鬲 丿乇 丿乇賵賳 賵丕賯毓丕賸 賳跇丕丿倬乇爻鬲 賳蹖爻鬲賳責 賲诏賴 賲乇丿賴丕 賵 夭賳鈥屬囏� 丿乇亘丕乇踿 夭賳丿诏蹖 噩賳爻蹖鈥屫促堎� 丕睾乇丕賯 賳賲蹖鈥屭┵嗁嗀� 亘丕亘丕 賲丕 丕蹖賳丕 乇賵 賲蹖鈥屫堎嗃屬�. 賱丕夭賲 賳蹖爻鬲 亘蹖丕蹖 亘丕 乇賵卮 禺丕氐鬲 丕蹖賳丕 乇賵 孬丕亘鬲 讴賳蹖. 亘毓丿 丕賵丕蹖賱 亘賴 禺賵丿賲 鬲卮乇 賲蹖鈥屫藏� 讴賴 禺亘 氐亘乇 讴賳貙 丿丕乇賴 丕夭 趩蹖夭賴丕蹖 爻丕丿賴 賵 賵丕囟丨 卮乇賵毓 賲蹖鈥屭┵嗁� 讴賴 亘乇爻賴 亘賴 趩蹖夭賴丕蹖 毓賲蹖賯鈥屫�. 丕夭 賴賲賵賳 丕賵賱 讴賴 賳賲鬲賵賳賴 亘蹖丕丿 亘诏賴貙 丕賵賳賲 賴賲趩蹖賳 賲賵囟賵毓蹖 讴賴 丕蹖賳鈥屬傌� 噩丿蹖丿賴 賵 卮丕蹖丿 亘乇丕蹖 禺蹖賱蹖鈥屬囏� 丕氐賱丕賸 丿乇讴卮 爻禺鬲 亘丕卮賴. (賲賳 禺賵丿賲 丿乇讴 丕賳丿讴蹖 丕夭 丕賯鬲氐丕丿 賵 讴賱丕賳鈥屫ж� 丿丕乇賲) 丕賲丕 丕丨爻丕爻 讴乇丿賲 賳賵蹖爻賳丿賴 丿丕乇賴 夭賵乇 夭蹖丕丿蹖 賲蹖鈥屫操嗁� 讴丕乇卮 乇賵 亘夭乇诏 賵 毓賱賲蹖 賳卮賵賳 亘丿賴貙 丿乇丨丕賱蹖鈥屭┵� 丿乇 賴乇 賮氐賱 賮賯胤 亘賴 賲亘丕丨孬卮 賵乇賵丿 賲蹖鈥屭┵嗁�. 賳賲蹖鈥屫堎嗁� 丕夭 賮乇囟蹖丕鬲卮 禺賵亘 丿賮丕毓 讴賳賴. 賳賲蹖鈥屫堎嗁� 丕蹖丿賴鈥屬囏ж� 乇賵 亘爻胤 亘丿賴. 賮賯胤 亘賴鬲 蹖賴 爻乇蹖 賮讴鬲 賵丕囟丨 賵 亘丿蹖賴蹖 賲蹖鈥屫� 讴賴 鬲賵 賲鬲賯丕毓丿 卮蹖 趩蹖夭蹖 讴賴 賲蹖鈥屭� 丿乇爻鬲賴. (賵 禺亘 丿乇爻鬲賴! 鬲賵蹖 丕蹖賳 亘丨孬蹖 賳丿丕乇賲) 丕賲丕 賳賵蹖爻賳丿賴 噩夭 亘蹖丕賳 賵 鬲卮乇蹖丨 乇賵卮 鬲丨賯蹖賯卮 (丕诏賴 丕蹖賳胤賵乇蹖 賲蹖鈥屫促� 诏賮鬲) 讴丕乇 禺丕氐蹖 賳賲蹖鈥屭┵嗁�. 噩丕賴丕蹖蹖 讴賴 亘賴 鬲丨賯蹖賯丕鬲鈥屫促堎� 乇蹖夭 賲蹖鈥屫促� 賲賳 亘蹖卮鬲乇 賴蹖噩丕賳鈥屫藏� 賲蹖鈥屫促� 賵賱蹖 蹖賴賵 賲蹖鈥屫屫� 禺蹖賱蹖 爻乇蹖毓 丕夭卮賵賳 賲蹖鈥屭柏簇� 蹖丕 丕氐賱丕賸 亘蹖鈥屫必ㄘ� 丨乇賮 賲蹖鈥屫藏�. 賮賯胤 鬲賵 乇賵 亘賴 亘丕丿賽 丕胤賱丕毓丕鬲 賲蹖鈥屫ㄘ池� 讴賴 賳賮賴賲蹖 禺賵丿賽 賳賵蹖爻賳丿賴 賳賲蹖鈥屫堎嗁� 趩蹖夭 亘蹖卮鬲乇 賵 噩丿蹖丿鬲乇蹖 亘賴卮 丕囟丕賮賴 讴賳賴. 卮丕蹖丿 鬲賱丕卮卮 毓丕賲丿丕賳賴 賴賲 賳亘賵丿賴 賴丕貙 卮丕蹖丿 丕夭 爻乇 匕賵賯 禺賵丿卮 蹖丕 噩賵賵賳蹖卮 亘賵丿賴貙 趩賲丿賵賳賲. 讴丕乇 卮亘蹖賴 乇爻丕賱踿 丿讴鬲乇丕爻鬲 鬲丕 蹖賴 讴鬲丕亘 賲丿賵賳 賳馗乇蹖.
賲賳 鬲賵蹖 丕蹖賳 噩丿丕賱 丿乇賵賳蹖 丿乇亘丕乇踿 賳蹖鬲 賵 乇賵蹖讴乇丿 賳賵蹖爻賳丿賴 亘賵丿賲 賵 賴賳賵夭賲 賴爻鬲賲 賵 亘賴 賳馗乇賲 禺賵丕賴賲 亘賵丿. 賴賲賴 丿乇賵睾 賲蹖鈥屭堐屬嗀� 丨鬲蹖 賳賵蹖爻賳丿踿 讴鬲丕亘 賴賲賴 丿乇賵睾 賲蹖鈥屭堐屬嗀�.
October 7, 2018
賰鬲丕亘 賲賮賷丿 賷賯丿賲 毓丿丿 賲賳 丕賱賲毓賱賵賲丕鬲 丕賱噩賷丿丞 賱賰賳 賰丕賳 賷賲賰賳 丕禺鬲氐丕乇賴 賱賱賳氐賮 鬲賯乇賷亘丕賸 丿賵賳 兀賳 賷賵孬乇 匕賱賰 毓賱賶 丕賱賲丨鬲賵賶
April 17, 2018
I am now convinced that Google searches are the most important data set ever collected on the human psyche. writes the author early on & he shows why. (Google trends is available to all here: ) He also checked other big data sets including Wikipedia, Facebook, Pornhub, & even Stormfront, the largest racist site. What he found was really interesting & it will help harden the soft, social sciences. It's a new frontier.
He points out problems with traditional reporting. In the section about child abuse & abortions, Google searches suggest that child abuse does increase during economic downturns while gov't figures incorrectly show little change. Closing abortion clinics doesn't stop them, it simply leads to more self-induced abortions. Both happen off the books, but there is now convincing supporting data to show us what we need to address & make more informed decisions with resources.
Big data has an advantage over every other type of survey because few realize it is being collected, so we don't lie to make ourselves look better. It's also anonymous & aggregate, so caution needs to be used when forming conclusions. For instance, based on Pornhub searches, the author concludes that about 5% of men are gay because they searched for gay porn. That seemed a reasonable conclusion until he pointed out that 15% of women search for rape porn. Does that mean they want to be raped? The author says of course not & makes a big deal out of the difference between fantasy & reality. That makes me question his first conclusion, although it seems about right.
Gut reactions are often wrong & he provides several examples where it's wrong due to cognitive biases. He also points out "The Curse of Dimensionality". Given large enough sets of data, there will be correlations just through chance. For instance, there are graphs that show how closely autism diagnoses track with organic food sales or Jenny McCarthy's popularity. Separating these out is a whole other problem.
Big Data only gives us trends that we need to examine. We can't use it on the individual level. While 1000 people searched for how to kill their girl friend, only 1 girl was killed in his example. That's horrific & might have been stopped if someone had looked at his search history, but do we give up everyone's privacy for a 1 in 1000 chance that we might prevent a murder? Some might be willing, but I'm not, so we also have new questions to address.
The audio book was well narrated & I didn't miss the graphs too much. They're provided in the extra material, but weren't handy when I was listening & the book took that into account for the most part. Highly recommended in either format.
He points out problems with traditional reporting. In the section about child abuse & abortions, Google searches suggest that child abuse does increase during economic downturns while gov't figures incorrectly show little change. Closing abortion clinics doesn't stop them, it simply leads to more self-induced abortions. Both happen off the books, but there is now convincing supporting data to show us what we need to address & make more informed decisions with resources.
Big data has an advantage over every other type of survey because few realize it is being collected, so we don't lie to make ourselves look better. It's also anonymous & aggregate, so caution needs to be used when forming conclusions. For instance, based on Pornhub searches, the author concludes that about 5% of men are gay because they searched for gay porn. That seemed a reasonable conclusion until he pointed out that 15% of women search for rape porn. Does that mean they want to be raped? The author says of course not & makes a big deal out of the difference between fantasy & reality. That makes me question his first conclusion, although it seems about right.
Gut reactions are often wrong & he provides several examples where it's wrong due to cognitive biases. He also points out "The Curse of Dimensionality". Given large enough sets of data, there will be correlations just through chance. For instance, there are graphs that show how closely autism diagnoses track with organic food sales or Jenny McCarthy's popularity. Separating these out is a whole other problem.
Big Data only gives us trends that we need to examine. We can't use it on the individual level. While 1000 people searched for how to kill their girl friend, only 1 girl was killed in his example. That's horrific & might have been stopped if someone had looked at his search history, but do we give up everyone's privacy for a 1 in 1000 chance that we might prevent a murder? Some might be willing, but I'm not, so we also have new questions to address.
The audio book was well narrated & I didn't miss the graphs too much. They're provided in the extra material, but weren't handy when I was listening & the book took that into account for the most part. Highly recommended in either format.
October 20, 2018
"鬲丨賲賱 亘毓囟 丕賱賲氐丕丿乇 丕賱賲鬲氐賱丞 亘丕賱廿賳鬲乇賳鬲 丕賱賳丕爻 毓賱賶 丕賱丕毓鬲乇丕賮 亘兀卮賷丕亍 賱丕 賷毓鬲乇賮賵賳 亘賴丕 賮賷 兀賷 賲賰丕賳 丌禺乇貙 兀賳賴丕 亘賲孬丕亘丞 賲氐賱 丕賱丨賯賷賯丞 丕賱乇賯賲賷丞. 禺匕 毓賲賱賷丕鬲 亘丨孬 睾賵睾賱貙 賵鬲匕賰乇 丕賱馗乇賵賮 丕賱鬲賷 鬲噩毓賱 丕賱賳丕爻 兀賰孬乇 氐丿賯賸丕: 賲鬲氐賱賷賳 亘丕賱廿賳鬲乇賳鬲貙 賵丨丿賴賲貙 賱丕 賷賵噩丿 卮禺氐 賷噩乇賷 丿乇丕爻丞 丕爻鬲賯氐丕卅賷丞."
賮賷 馗賱 丕賱孬賵乇丞 丕賱鬲賰賳賵賱賵噩賷丞 丕賱賲鬲爻丕乇毓丞貙 兀氐亘丨 丕賱亘丨孬 毓賳 丕賱賲毓賱賵賲丕鬲 兀爻賴賱 賵兀賷爻乇 賲賲丕 賰丕賳 賯亘賱賸丕. 賮廿賲賰丕賳賳丕 丕賱丌賳 兀賳 賳毓乇賮 賰賲 卮禺氐 賲鬲氐賱 亘丕賱廿賳鬲乇賳鬲 賷賵賲賷賸丕貙 賵賲丕 賴賷 丕賱賲賵丕賯毓 丕賱兀賰孬乇 鬲氐賮丨賸丕貙 賵廿賱禺 賲賳 丕賱亘賷丕賳丕鬲. 賵亘丕賱鬲兀賰賷丿 賷賲賰賳賳丕 兀賳 賳毓乇賮 丕賱賲夭賷丿 毓賳 胤亘賷毓丞 丕賱亘卮乇 賲賳 禺賱丕賱 賲丕 賷噩乇賵賳賴 賲賳 兀亘丨丕孬 毓賱賶 丕賱賲賵賯毓 丕賱兀賰孬乇 卮賴乇丞 賮賷 丕賱毓丕賱賲 "睾賵睾賱". 賵亘丕賱鬲丕賱賷 爻賳毓乇賮 兀賳 丕賱亘卮乇 賷賰匕亘賵賳貙 賵賷爻鬲鬲乇賵賳 禺賱賮 丕賱毓丿賷丿 賲賳 丕賱兀賯賳毓丞 丕賱鬲賷 鬲禺賮賷 乇睾亘丕鬲 賵兀賵賴丕賲 賵兀賮賰丕乇 賯丿 鬲亘丿賵 賱賳丕 氐丕丿賲丞. 賰賲 賲乇丞 賰賳鬲 鬲噩賱爻 賵丨賷丿賸丕 賲毓 丨丕爻賵亘賰 丕賱卮禺氐賷 賵鬲胤乇丨 毓賱賶 "睾賵睾賱" 丕賱毓丿賷丿 賲賳 丕賱兀爻卅賱丞 賵丕賱兀賮賰丕乇 丕賱鬲賷 鬲丿賵乇 賮賷 亘丕賱賰 賵鬲噩乇賷 兀亘丨丕孬賸丕貙 賵丕賱鬲賷 賱丕 賷毓賱賲 毓賳賴丕 兀賷 兀丨丿 毓賱賶 賵噩賴 丕賱兀乇囟責 亘丕賱胤亘毓貙 毓丿丿 賱丕 賷氐丨賶 賲賳 丕賱賲乇丕鬲!
賮賷 賴匕丕 丕賱賰鬲丕亘貙 賷亘賷賳 賱賳丕 爻賷孬 爻賷鬲賮賳夭- 丿丕賮賷丿賵賷鬲夭 兀賴賲賷丞 丕賱亘賷丕賳丕鬲 丕賱囟禺賲丞貙 賵亘賷丕賳丕鬲 "睾賵睾賱" 賮賷 丕賱賰卮賮 毓賳 丕賱賰孬賷乇 賲賳 丕賱兀賲賵乇 丕賱賲禺鬲賱賮丞 賵丕賱賲鬲賳賵毓丞 賮賷 丨賷丕丞 丕賱亘卮乇貙 賵賰賷賮 兀賳 賳鬲丕卅噩 亘賷丕賳丕鬲 "睾賵睾賱" 鬲禺鬲賱賮 賮賷 賳鬲丕卅噩賴丕 毓賳 丕賱丿乇丕爻丕鬲 丕賱丕爻鬲賯氐丕卅賷丞 賵丕賱丕爻鬲亘賷丕賳丕鬲貙 賵匕賱賰 亘兀賳 丕賱亘卮乇 賷賲賷賱賵賳 賱賱賰匕亘! 賲賲丕 賷賮鬲丨 丕賱亘丕亘 兀賲丕賲賳丕 賱賳賵毓 噩丿賷丿 賲賳 丕爻鬲禺乇丕噩 丕賱賲毓賱賵賲丕鬲貙 兀賱丕 賵賴賵 "丕賱亘賷丕賳丕鬲 丕賱囟禺賲丞".
"賱丕 賷禺亘乇 賰孬賷乇 賲賳 丕賱賳丕爻 丕賱丿乇丕爻丕鬲 丕賱丕爻鬲賯氐丕卅賷丞 毓賳 丕賱兀賮賰丕乇 賵丕賱鬲氐乇賮丕鬲 丕賱賲丨乇噩丞. 賷乇賷丿賵賳 兀賳 賷亘丿賵丕 噩賷丿賷賳 毓賱賶 丕賱乇睾賲 賲賳 兀賳 賲毓馗賲 丕賱丿乇丕爻丕鬲 丕賱丕爻鬲賯氐丕卅賷丞 賱丕 鬲匕賰乇 兀爻賲丕亍 丕賱兀卮禺丕氐 賵賴匕丕 賲丕 賷爻賲賶 亘丕賱丕賳丨賷丕夭 賱賱賲賯亘賵賱 丕噩鬲賲丕毓賷賸丕."
賰鬲丕亘 賲賲鬲毓 丨賯賸丕貙 賵賮賰乇丞 噩丿賷丿丞 毓賱賷賾 賰賱賷賸丕.
"賷賰匕亘 丕賱賳丕爻 丨賵賱 毓丿丿 丕賱賰丐賵爻 丕賱鬲賷 丕丨鬲爻賵賴丕 賮賷 胤乇賷賯賴賲 廿賱賶 丕賱亘賷鬲貙 賵賷賰匕亘賵賳 丨賵賱 毓丿丿 賲乇丕鬲 匕賴丕亘賴賲 廿賱賶 丕賱賳丕丿賷 丕賱乇賷丕囟賷貙 賵丨賵賱 鬲賰賱賮丞 鬲賱賰 丕賱兀丨匕賷丞 丕賱噩丿賷丿丞貙 賵賮賷賲丕 廿匕丕 賰丕賳賵丕 賯丿 賯乇兀賵丕 匕丕賰 丕賱賰鬲丕亘. 賷鬲氐賱賵賳 賲鬲毓匕乇賷賳 亘丕賱賲乇囟 賮賷 丨賷賳 兀賳賴賲 兀氐丨丕亍貙 賵賷賯賵賱賵賳 兀賳賴賲 爻賷亘賯賵賳 毓賱賶 鬲賵丕氐賱 賮賷 丨賷賳 兀賳賴賲 賱賳 賷亘賯賵丕 毓賱賶 鬲賵丕氐賱貙 賵賷賯賵賱賵賳 兀賳 丕賱兀賲乇 賱丕 賷鬲毓賱賯 亘賰 賮賷 丨賷賳 兀賳賴 賷鬲毓賱賯 亘賰貙 賵賷賯賵賱賵賳 兀賳賴賲 賷丨亘賵賳賰 賮賷 丨賷賳 兀賳賴賲 賱丕 賷丨亘賵賳賰貙 賵賷賯賵賱賵賳 兀賳賴賲 爻毓丿丕亍 亘賷賳賲丕 賴賲 鬲毓爻丕亍貙 賵賷賯賵賱賵賳 兀賳賴賲 賲毓噩亘賵賳 亘丕賱賳爻丕亍 賮賷 丨賷賳 兀賳賴賲 賲毓噩亘賵賳 賮賷 丕賱丨賯賷賯丞 亘丕賱乇噩丕賱. 賷賰匕亘 丕賱賳丕爻 毓賱賶 兀氐丿賯丕卅賴賲貙 賵賷賰匕亘賵賳 毓賱賶 乇丐爻丕卅賴賲 賮賷 丕賱毓賲賱貙 賵賷賰匕亘賵賳 毓賱賶 兀胤賮丕賱賴賲貙 賵賷賰匕亘賵賳 毓賱賶 丌亘丕卅賴賲貙 賵賷賰匕亘賵賳 毓賱賶 兀夭賵丕噩賴賲貙 賵賷賰匕亘賵賳 毓賱賶 夭賵噩丕鬲賴賲貙 賵賷賰匕亘賵賳 毓賱賶 兀賳賮爻賴賲. 賵賷賰匕亘賵賳 亘賰賱 鬲兀賰賷丿 毓賱賶 丕賱丿乇丕爻丕鬲 丕賱丕爻鬲賯氐丕卅賷丞"
兀賲丕 亘禺氐賵氐 丕賱鬲乇噩賲丞貙 賮兀賳 夭賵噩賷 賵卮乇賷賰賷 賵氐丿賷賯賷 兀丨賲丿 丨爻賷賳 卮丕賴賷賳 賯丕賲 亘鬲乇噩賲鬲賴 賱賲丕 賵噩丿賴 賮賷賴 賲賳 賮丕卅丿丞 賵賮賰乇 噩丿賷丿 賷爻鬲丨賯 兀賳 賷胤賱賾毓 毓賱賷賴 丕賱賯丕乇卅 丕賱毓乇亘賷. 賵賴匕賴 丕賱鬲噩乇亘丞 丕賱兀賵賱賶 賱賴 賮賷 丕賱鬲乇噩賲丞貙 賵丕賱鬲賷 賳丿毓賵 丕賱賱賴 兀賳 賷爻鬲賰賲賱賴丕 賵賷胤賵賾乇賴丕 賮賷 鬲乇噩賲丞 兀毓賲丕賱 兀禺乇賶 鬲孬乇賷 丕賱賲賰鬲亘丞 丕賱毓乇亘賷丞.
賵賴丕 賴賵 乇丕亘胤 丕賱賰鬲丕亘貙 賲鬲丕丨 廿賱賰鬲乇賵賳賷賸丕 賵賲噩丕賳賸丕 賱賰賱 賲賳 賷乇睾亘 賮賷 賯乇丕卅鬲賴:
賵丕賱賱賴 賵賱賷 丕賱鬲賵賮賷賯.
...
June 5, 2017
This book could have used a good editor. It tries to be a Gladwell-type of book without fully succeeding. Issue 1 is that the anecdotal stories are not fleshed out enough to really draw you in like Gladwell does. This causes much of the book to come across as a list of facts, and it gets pretty old by the midway point.
The other issue is a growing trend among people writing data books. They want to write in a colloquial style to make it seem informal and easy to read. They don't want to scare off people with talk of algorithms and things like that.
Unfortunately, using tons of sentence fragments and colloquial phrases only makes a book like this harder to read. It's precision and clarity that make books easy to understand. Introducing ambiguity in order to sound like a friendly conversation is exactly the wrong approach.
Overall, there are a bunch of interesting facts in here. I think Seth gets a bunch wrong, though, in not understanding fully why certain search terms are used.
The other issue is a growing trend among people writing data books. They want to write in a colloquial style to make it seem informal and easy to read. They don't want to scare off people with talk of algorithms and things like that.
Unfortunately, using tons of sentence fragments and colloquial phrases only makes a book like this harder to read. It's precision and clarity that make books easy to understand. Introducing ambiguity in order to sound like a friendly conversation is exactly the wrong approach.
Overall, there are a bunch of interesting facts in here. I think Seth gets a bunch wrong, though, in not understanding fully why certain search terms are used.
November 7, 2017
Maybe everyone does lie. But they don鈥檛 lie all the time. Stephens-Davidowitz makes the good point that asking people directly doesn鈥檛 always, in fact may not often, yield true answers. People have their own reasons for answering pollsters untruthfully, but it is clear that this is a documented fact. People sometimes lie to pollsters.
Stephens-Davidowitz was told by mentors and advisors not to consider Google searches worthwhile data, but the more he looked at it, the more he was convinced that Google searches contained the best data for determining what people are concerned about. He has uncovered some interesting trends that are not apparent through direct questioning because people are sometimes ashamed of their fears, feelings, prejudices, and predilections.
铀�
I didn鈥檛 really like this book. Partly the reason is because I listened to it, and Stephens-Davidowitz gives charts, graphs, data points that obviously cannot be represented in the audio version. These usually help me to grasp things easily and maybe bypass pages of material that is not as interesting to me. It wasn鈥檛 that his material was hard, it was that I oftentimes did not like what he was talking about. He had a tendency to focus on deviant behavior, e.g., sexual predators, abuse, porn, etc. One might make the argument that these behaviors are important to understand and therefore worth looking at. Possibly. However, if 鈥榚verybody lies,鈥� one might make the argument that we do not have to look at deviance to find untruthfulness.
What we discover is that to test Stephens-Davidowitz鈥檚 thesis that 鈥榚verybody lies,鈥� we have to spend quite a lot of time with statistics and creating studies, or as he is wont to do, studying big data. Big data probably irons out discrepancies in the reasons for our Google searches, e.g., that it is not me that is interested in the herpes virus, it is my brother, because in the end it doesn鈥檛 matter why we did the search; what matters is that we did the search. Besides, maybe I鈥檓 lying about my brother having the virus, but my interest in the topic is not a lie.
Stephens-Davidowitz has made a career so far out of the study of big data, showing us ways to slice and dice it so that it is useful to our view of the world. Only thing is, I am not as interested in what big data tells us as he is. He鈥檇 trained as an economist, and towards the end of the book he hit a couple of areas I did find more interesting, like the notion of regression discontinuity, a term used to describe a statistical tool created to measure the outcomes of people very close to some arbitrary cut-off.** S-D talks about using this tool on federal inmates, discovering criminals treated more harshly committed more crimes upon their release. But S-D also studied students on either side of the admissions cut-off for the prestigious Stuyvesant High School: those who attended Stuyvesant did not have a significant performance difference in later life than students who did not.
Apparently Stephens-Davidowitz went into data science because of Freakonomics, the bestselling book by Steven D. Levitt. He believes that many of the next generation of scientists in every field will be data scientists. I did finish the audiobook, another study he took note of in the last pages. Apparently few readers finish 鈥榯reatises鈥� by economists. He believes this is his big contribution to our knowledge base, and there is no doubt his contrariness did highlight ways big data can be used effectively.
If I may be so bold, I might be able to suggest a reason why many female readers may not be as interested in the material presented, or in Stephens-Davidowitz himself (he was/is apparently looking for a girlfriend). Stay away from the deviant sex stuff, Seth. It may interest you but I can guarantee that fewer women are going to find that appealing or reassuring conversation or reading material.
An interesting corollary to this economists鈥� data view is the question of whether the truth matters, which is how I came to pick up this book. Recently on PBS鈥� The Third Rail with Ozy, Carlos Watson asked whether the truth matters. At first blush the answer seems obvious, and two sides debated this question. One side said of course truth matters鈥ut most of us know one man鈥檚 truth to be another man鈥檚 lie. The other side said 鈥榚verybody lies.鈥� It got me to thinking鈥 do think the two ways of coming to the notion of lying dovetail at some point, and one has to conclude that truth may not matter as much as we think. What matters is what we believe to be true.
Finally, it appears Stephens-Davidson agrees to some degree with Cathy O'Neill, author of Weapons of Math Destruction, in that he agrees you best not let algorithms run without human tweaking and interference. The best outcomes are delivered when humans apply their particular observations and knowledge and expertise along with big data.
** S-D describes it this way:
Stephens-Davidowitz was told by mentors and advisors not to consider Google searches worthwhile data, but the more he looked at it, the more he was convinced that Google searches contained the best data for determining what people are concerned about. He has uncovered some interesting trends that are not apparent through direct questioning because people are sometimes ashamed of their fears, feelings, prejudices, and predilections.
铀�
I didn鈥檛 really like this book. Partly the reason is because I listened to it, and Stephens-Davidowitz gives charts, graphs, data points that obviously cannot be represented in the audio version. These usually help me to grasp things easily and maybe bypass pages of material that is not as interesting to me. It wasn鈥檛 that his material was hard, it was that I oftentimes did not like what he was talking about. He had a tendency to focus on deviant behavior, e.g., sexual predators, abuse, porn, etc. One might make the argument that these behaviors are important to understand and therefore worth looking at. Possibly. However, if 鈥榚verybody lies,鈥� one might make the argument that we do not have to look at deviance to find untruthfulness.
What we discover is that to test Stephens-Davidowitz鈥檚 thesis that 鈥榚verybody lies,鈥� we have to spend quite a lot of time with statistics and creating studies, or as he is wont to do, studying big data. Big data probably irons out discrepancies in the reasons for our Google searches, e.g., that it is not me that is interested in the herpes virus, it is my brother, because in the end it doesn鈥檛 matter why we did the search; what matters is that we did the search. Besides, maybe I鈥檓 lying about my brother having the virus, but my interest in the topic is not a lie.
Stephens-Davidowitz has made a career so far out of the study of big data, showing us ways to slice and dice it so that it is useful to our view of the world. Only thing is, I am not as interested in what big data tells us as he is. He鈥檇 trained as an economist, and towards the end of the book he hit a couple of areas I did find more interesting, like the notion of regression discontinuity, a term used to describe a statistical tool created to measure the outcomes of people very close to some arbitrary cut-off.** S-D talks about using this tool on federal inmates, discovering criminals treated more harshly committed more crimes upon their release. But S-D also studied students on either side of the admissions cut-off for the prestigious Stuyvesant High School: those who attended Stuyvesant did not have a significant performance difference in later life than students who did not.
Apparently Stephens-Davidowitz went into data science because of Freakonomics, the bestselling book by Steven D. Levitt. He believes that many of the next generation of scientists in every field will be data scientists. I did finish the audiobook, another study he took note of in the last pages. Apparently few readers finish 鈥榯reatises鈥� by economists. He believes this is his big contribution to our knowledge base, and there is no doubt his contrariness did highlight ways big data can be used effectively.
If I may be so bold, I might be able to suggest a reason why many female readers may not be as interested in the material presented, or in Stephens-Davidowitz himself (he was/is apparently looking for a girlfriend). Stay away from the deviant sex stuff, Seth. It may interest you but I can guarantee that fewer women are going to find that appealing or reassuring conversation or reading material.
An interesting corollary to this economists鈥� data view is the question of whether the truth matters, which is how I came to pick up this book. Recently on PBS鈥� The Third Rail with Ozy, Carlos Watson asked whether the truth matters. At first blush the answer seems obvious, and two sides debated this question. One side said of course truth matters鈥ut most of us know one man鈥檚 truth to be another man鈥檚 lie. The other side said 鈥榚verybody lies.鈥� It got me to thinking鈥 do think the two ways of coming to the notion of lying dovetail at some point, and one has to conclude that truth may not matter as much as we think. What matters is what we believe to be true.
Finally, it appears Stephens-Davidson agrees to some degree with Cathy O'Neill, author of Weapons of Math Destruction, in that he agrees you best not let algorithms run without human tweaking and interference. The best outcomes are delivered when humans apply their particular observations and knowledge and expertise along with big data.
** S-D describes it this way:
鈥淎ny time there is precise number that divides people into two different groups, a discontinuity, economists can compare, or regress, the outcomes of people very very close to the cut off.鈥�
June 12, 2017
Acertei em cheio nessa leitura! Seth Stephens-Davidowitz apresenta uma an谩lise de como as pessoas se comportam, na mesma linha do e do . Mas enquanto Signal and the Noise fala de tend锚ncias de dados e Dataclisma fala do comportamento das pessoas dentro do OkCupid!, Everybody Lies fala de como as pessoas se comportam em geral.
O autor usa uma s茅rie de dados de forma bastante inovadora, como tend锚ncias de buscas no Google (onde ele trabalha), buscas no PornHub, Facebook e outras fontes de big data para fazer o que ele chama de "sociologia de verdade" ou sociologia baseada em evid锚ncias. Os dados que ele mostra sobre preconceito (buscas por temas preconceituosos), inseguran莽a de auto-imagem, inseguran莽as em rela莽茫o aos filhos e afins mostram uma imagem bem mais crua e feia da sociedade do que o que pintamos com postagens em Facebook e Instagram.
Outros revelam informa莽玫es no m铆nimo interessantes, sobre a diferen莽a que se formar em Harvard pode fazer (nenhuma, o ponto parece estar em quem se forma), onde criar os filhos, como aumentar as chances de sucesso em um encontro... O livro lembra bastante uma vers茫o mais nova e, na minha opini茫o, mais curiosa da abordagem inovadora de Freakonomics.
Se voc锚 n茫o est谩 interessado na revolu莽茫o que o registro e a disponibilidade de dados est谩 causando no mundo, e no estrago que empresas e governos conseguem fazer com o controle que t锚m sobre a informa莽茫o, no m铆nimo vai curtir o livro pelos fatos curiosos e m贸rbidos que ele levanta dos dados. Saber por exemplo que o n煤mero de homens que buscam como fazer bem sexo oral nas mulheres 茅 o mesmo que busca por como fazer sexo oral em si mesmo fala muito sobre como as pessoas pensam. Um livro para todos os gostos.
O autor usa uma s茅rie de dados de forma bastante inovadora, como tend锚ncias de buscas no Google (onde ele trabalha), buscas no PornHub, Facebook e outras fontes de big data para fazer o que ele chama de "sociologia de verdade" ou sociologia baseada em evid锚ncias. Os dados que ele mostra sobre preconceito (buscas por temas preconceituosos), inseguran莽a de auto-imagem, inseguran莽as em rela莽茫o aos filhos e afins mostram uma imagem bem mais crua e feia da sociedade do que o que pintamos com postagens em Facebook e Instagram.
Outros revelam informa莽玫es no m铆nimo interessantes, sobre a diferen莽a que se formar em Harvard pode fazer (nenhuma, o ponto parece estar em quem se forma), onde criar os filhos, como aumentar as chances de sucesso em um encontro... O livro lembra bastante uma vers茫o mais nova e, na minha opini茫o, mais curiosa da abordagem inovadora de Freakonomics.
Se voc锚 n茫o est谩 interessado na revolu莽茫o que o registro e a disponibilidade de dados est谩 causando no mundo, e no estrago que empresas e governos conseguem fazer com o controle que t锚m sobre a informa莽茫o, no m铆nimo vai curtir o livro pelos fatos curiosos e m贸rbidos que ele levanta dos dados. Saber por exemplo que o n煤mero de homens que buscam como fazer bem sexo oral nas mulheres 茅 o mesmo que busca por como fazer sexo oral em si mesmo fala muito sobre como as pessoas pensam. Um livro para todos os gostos.
November 27, 2019
讴鬲丕亘 丨丕囟乇 -亘丕 丕蹖賳 毓賳賵丕賳 噩匕丕亘卮- 丿乇 賲賵乇丿 讴丕乇亘乇丿賴丕蹖 亘蹖诏 丿蹖鬲丕 (讴賱丕賳 丿丕丿賴) 賵 丿丕丿賴鈥屭┴з堐� 丿乇 毓賱賵賲 丕噩鬲賲丕毓蹖 賵 丕賯鬲氐丕丿蹖鈥屫池�. 賳賵蹖爻賳丿賴 丕亘鬲丿丕 亘丕 賲賯丕蹖爻賴鈥屰� 賳鬲丕蹖噩 丨丕氐賱 丕夭 倬蹖賲丕蹖卮鈥屬囏� 賵 丌賲丕乇 賵 丕胤賱丕毓丕鬲 賲賵噩賵丿 賳卮丕賳 賲蹖鈥屫囏� 倬蹖賲丕蹖卮鈥屬囏� 賲蹖鈥屫堌з嗁嗀� 诏賲乇丕賴鈥屭┵嗁嗀� 亘丕卮賳丿 (亘賴 丿賱蹖賱 鬲賲丕蹖賱 丕賳爻丕賳鈥屬囏� 亘賴 禺賵亘 噩賱賵賴 丿丕丿賳 禺賵丿). 爻倬爻 丿乇 趩賳丿蹖賳 賮氐賱 讴丕乇亘乇丿賴丕蹖 丿丕丿賴鈥屭┴з堐� 乇丕 賲賮氐賱丕 鬲卮乇蹖丨 賲蹖鈥屭┵嗀� 賵 丿乇 丕賳鬲賴丕 亘賴 賲丨丿賵丿蹖鬲鈥屬囏� 賵 賲卮讴賱丕鬲 賳丕卮蹖 丕夭 賮乇丕诏蹖乇 卮丿賳 丿丕丿賴鈥屭┴з堐� 賲蹖鈥屬矩必ж藏�. 卮禺氐丕 丕夭 賲亘丕丨孬 賲乇亘賵胤 亘賴 卮賴賵丿 賵 鬲賮丕賵鬲 賴賲亘爻鬲诏蹖 賵 毓賱蹖鬲 亘爻蹖丕乇 賱匕鬲 亘乇丿賲. 亘爻蹖丕乇 噩匕丕亘 賵 倬乇讴卮卮 賳賵卮鬲賴 卮丿賴 賵 鬲乇噩賲賴鈥屰� 賲丨卮乇蹖 丿丕乇丿. 亘賴 賳馗乇 賲賳 噩匕丕亘蹖鬲 賲亘丕丨孬 賲胤乇丨 卮丿賴 丿乇 讴鬲丕亘 亘賴 賯丿乇蹖鈥屫池� 讴賴 毓賱丕賵賴 亘乇 賲鬲禺氐氐蹖賳 丿丕丿賴貙 毓賱丕賯賲賳丿丕賳 亘賴 賲賵囟賵毓丕鬲 丕賯鬲氐丕丿蹖 賵 丕噩鬲賲丕毓蹖 賳蹖夭 丕夭 禺賵丕賳丿賳 讴鬲丕亘 賱匕鬲 亘亘乇賳丿. 亘賴 卮丿鬲 倬蹖卮賳賴丕丿 賲蹖鈥屫促堌�.
March 8, 2020
鈥濧 kr茅tai Epimenid茅sz a k枚vetkez艖 halhatatlan kijelent茅st tette: Minden kr茅tai hazudik.鈥�
Erre az amerikai Seth Stephens-Davidowitz a k枚vetkez艖 halhatatlan kijelent茅st tette: Mindenki hazudik. Na, Epimenid茅sz, most mit l茅psz?
K眉l枚nben meg t茅nyleg. Hazudunk arr贸l, mennyit szeretkez眉nk, mit gondolunk a gyerekeinkr艖l, mennyire vagyunk toler谩nsak a kisebbs茅gekkel. Hazudunk a facebookon, a szociol贸giai kutat谩sokban, az 谩ll谩sinterj煤n. Hazudunk m谩snak, hazudunk magunknak. 脡s hogy kerek legyen: hazudunk a k枚nyvc铆mekben is. J贸, legyek megenged艖: csak sum谩kolunk, hogy jobb elad谩si mutat贸kat tudjunk el茅rni.
Mert ez a k枚nyv igaz谩b贸l nem arr贸l sz贸l, hogy mindenki hazudik, b谩rmit 谩ll铆tson is a c铆me*. Persze arr贸l is, hisz egyik k枚vetkeztet茅se, hogy a Google-keres茅sek sor谩n olyan dolgokat is megvallunk, amiket k眉l枚nben sehol m谩shol. De ez 枚nmag谩ban m茅g egy el茅g puha hipot茅zis, hisz ki tudja, a sok h眉lyes茅g, amit a gugliba beg茅pel眉nk, komolyan vehet艖-e. Ok茅, elfogadom, a banki hitelfelv茅teln茅l hajlamosak vagyunk j贸val stabilabbnak lefesteni az 茅lethelyzet眉nket, mint amilyen val贸j谩ban, 茅s ritk谩n tessz眉k ki ny铆ltan a k枚z枚ss茅gi oldalakra, hogy a PornHub-r贸l szoktunk esti mes茅t v谩lasztani magunknak. (脡n persze nem. De TI igen. H枚h.) Ugyanakkor ha egy f茅rfi a Google-ban r谩keres, hogyan tudn谩 枚nmag谩t, khm, a sz谩j谩val, khm, nem is folytatom, az nem felt茅tlen眉l azt jelenti, hogy komolyan foglalkoztatja a kivitelez茅s 鈥� lehet, csak olvasta Stephen-Davidowitzn谩l, hogy sokan ilyesmi ir谩nt 茅rdekl艖dnek, 茅s egyszer疟en meg枚li a k铆v谩ncsis谩g, ezt m茅gis ki k茅pes v茅ghez vinni. Sz贸val az, hogy ki hazudik 茅s ki mond igazat, bonyolult k茅rd茅s, legal谩bbis 贸vatosan kell teh谩t kezeln眉nk a szerz艖 azon 谩ll铆t谩s谩t, hogy az 艖 m贸dszere egyfajta 鈥瀒gazs谩gsz茅rum鈥�**.
Val贸j谩ban ez a k枚nyv himnusz a Big Data adatkutat贸ihoz. Seth Stephens-Davidowitz igazi k枚zponti 谩ll铆t谩sa (szerintem 茅s h谩la Istennek) nem a hazugs谩gokkal kapcsolatos, hanem azzal, hogy a Big Data olyan eszk枚z, ami a puszta m茅ret茅vel teljesen 谩talak铆tja a tudom谩nyos kutat谩st. Hiszen eddig az volt, hogy a t谩rsadalomtud贸soknak elk茅peszt艖en bonyolult 茅s k枚lts茅ges volt k铆s茅rletezni眉k 鈥� a k茅rd艖铆ves rendszerek, a csoportokat 茅s kontrollcsoportokat felhaszn谩l贸 kutat谩sok egyar谩nt hosszadalmas el艖k茅sz眉leteket ig茅nyeltek, r谩ad谩sul az eredm茅nyek gyakran ugyan煤gy k茅ts茅gbevonhat贸ak voltak, vagy nem 茅rt茅k meg a befektetett t艖k茅t. Ezzel szemben ma egy techc茅g naponta ak谩r t枚bb ezer 煤n 鈥濧/B tesztet***鈥� is lefuttathat kv谩zi automatikusan, minden probl茅ma n茅lk眉l, 茅s h谩la az internet elterjed茅s茅nek, a kutat贸k a Google t枚bb milli谩rdos adatb谩zis谩b贸l tudnak dolgozni, olyan inform谩ci贸kat h铆vva le a rendszerb艖l p谩r billenty疟 le眉t茅s茅vel (茅s n茅mi kreativit谩ssal), amir艖l p谩r 茅vtizede m茅g 谩lmodozni se mertek. Van egy hipot茅zis眉nk, miszerint Trump v谩laszt贸i rasszist谩bbak, mint a t枚bbiek? N茅zz眉k meg, a Google-ben hol 铆rt谩k be a 鈥瀗igger viccek鈥� kifejez茅st a keres艖ablakba, 茅s az 铆gy kapott t茅rk茅pet vess眉k 枚ssze a v谩laszt谩si t茅rk茅pekkel. Voil谩. 脡s hogy ezt meg tudjuk tenni, az forradalmas铆tja a t谩rsadalomtudom谩nyt, ami eddig puha volt, 谩m most - azzal, hogy ilyen szimpl谩n tudunk hipot茅ziseket ellen艖rizni - l茅nyegesen egzaktabb谩 v谩lik****. Persze 鈥� ezt a szerz艖 nem tagadja 鈥� ez semmik茅ppen sem jelenti azt, hogy a megszokott kiscsoportos k铆s茅rleteket 茅s felm茅r茅seket el lehet felejteni, puszt谩n azt, hogy a kett艖 egy眉tt haszn谩lva elk茅peszt艖en er艖s k枚vetkeztet茅seket fog eredm茅nyezni. 脡s ebben t枚k茅letesen egyet茅rtek vele.
脰sszess茅g茅ben ez a k枚nyv baromi informat铆v 茅s hat谩sos, ha azokat a passzusokat n茅zz眉k, amikor a titkolt, explicit el艖铆t茅letess茅gr艖l besz茅l, amelyek m茅g mindig hemzsegnek a nyugati civiliz谩ci贸 谩lcah谩l贸ja alatt. M谩r puszt谩n ez茅rt is 茅rdemes elolvasni. M谩sfel艖l viszont olyannyira k枚zpontba helyezi a szexualit谩s t茅mak枚r茅t, hogy azt m谩r neh茅z m谩snak 茅rtelmezni, mint vev艖csalogat谩snak. J贸, h谩t bevallom, ezek a r茅szek engem is sz贸rakoztattak, de az茅rt k枚zben t枚bbsz枚r elm茅l谩ztam azon, mennyire relev谩nsak az eff茅le inform谩ci贸k. Ha Seth Stephens-Davidowitz azt akarta bizony铆tani vel眉k, hogy t茅nyleg, de t茅nyleg mindenki hazudik, akkor jelentem, ez m谩r durv谩n 15-20 oldal ut谩n siker眉lt neki, a t枚bbi csak a tejsz铆nhab a gesztenyep眉r茅n. K眉l枚nben meg finom a tejsz铆nhab is. 脷gyhogy nem panaszkodom.
* Mondjuk ahhoz k茅pest, hogy a szerz艖 eredetileg a 鈥濵ekkora a p茅niszem?鈥� c铆mmel akarta piacra dobni ezt a term茅ket, a 鈥濵indenki hazudik鈥� 谩ll铆t谩s kifejezetten visszafogottnak t疟nik. Ugyanakkor 枚nmag谩ban jelzi, hogy Stephens-Davidowitz nem retten vissza egy kis marketingdinamit-haszn谩latt贸l.
** Jellemz艖 a m贸dszer korl谩tait illet艖en a p茅lda, amit a z谩rsz贸ban el艖cit谩l. Itt a szerz艖 hivatkozik egy matematikusra, aki abb贸l kiindulva, hogy a k枚nyvek els艖 fel茅b艖l t枚bbet id茅znek, mint a k枚nyvek m谩sodik fel茅b艖l, azt a k枚vetkeztet茅st vonta le, hogy az emberek kevesebb k枚nyvet olvasnak v茅gig, mint ahogy azt 谩ll铆tj谩k. Csin谩lt ilyen kis csecse statisztik谩t is a Big Data felhaszn谩l谩s谩val, amiben kisz谩molta, hogy az id茅zetek oldalsz谩moz谩s谩b贸l 铆t茅lve h谩ny sz谩zal茅k fejezte be v茅g眉l az adott m疟veket. (Piketty T艖k茅j茅t p茅ld谩ul 3%.) No most 茅n szint茅n gyakrabban id茅zek a k枚nyvek els艖 fel茅b艖l, mint a v茅g茅b艖l, m茅gpedig az茅rt, mert hajlamos vagyok az els艖 sz谩z oldalr贸l mindent ki铆rni, ami megtetszik, ut谩na viszont ellustulok, 茅s jobban megsz疟r枚m, mit 茅rdemes, mit nem. Lehet, a hipot茅zis ink谩bb ki枚tl艖je saj谩t olvas谩si szok谩sair贸l 谩rulkodik, mint az eny茅mr艖l.
*** Olyan tesztek ezek, amelyeket mondjuk a Google, az Amazon vagy a Facebook m谩r 茅vek 贸ta t枚meg茅vel futtat. P茅ld谩ul t枚bb ezer random kiv谩lasztott felhaszn谩l贸nak a k茅k egy bizonyos 谩rnyalat谩ban j枚n f枚l az oldal, m谩s felhaszn谩l贸knak meg egy m谩sik 谩rnyalattal. Amelyikre t枚bb kattint谩st gener谩l, az lesz az optim谩lis sz铆n.
**** 脡rdekes, hogy mindek枚zben a term茅szettudom谩nyok mintha vesz铆ten茅nek egzakts谩gukb贸l. Legal谩bbis a kvantumfizika vagy a h煤relm茅let kezd annyira elm茅leti lenni, ami k铆s茅rleti 煤ton m谩r nem is igazolhat贸.
Erre az amerikai Seth Stephens-Davidowitz a k枚vetkez艖 halhatatlan kijelent茅st tette: Mindenki hazudik. Na, Epimenid茅sz, most mit l茅psz?
K眉l枚nben meg t茅nyleg. Hazudunk arr贸l, mennyit szeretkez眉nk, mit gondolunk a gyerekeinkr艖l, mennyire vagyunk toler谩nsak a kisebbs茅gekkel. Hazudunk a facebookon, a szociol贸giai kutat谩sokban, az 谩ll谩sinterj煤n. Hazudunk m谩snak, hazudunk magunknak. 脡s hogy kerek legyen: hazudunk a k枚nyvc铆mekben is. J贸, legyek megenged艖: csak sum谩kolunk, hogy jobb elad谩si mutat贸kat tudjunk el茅rni.
Mert ez a k枚nyv igaz谩b贸l nem arr贸l sz贸l, hogy mindenki hazudik, b谩rmit 谩ll铆tson is a c铆me*. Persze arr贸l is, hisz egyik k枚vetkeztet茅se, hogy a Google-keres茅sek sor谩n olyan dolgokat is megvallunk, amiket k眉l枚nben sehol m谩shol. De ez 枚nmag谩ban m茅g egy el茅g puha hipot茅zis, hisz ki tudja, a sok h眉lyes茅g, amit a gugliba beg茅pel眉nk, komolyan vehet艖-e. Ok茅, elfogadom, a banki hitelfelv茅teln茅l hajlamosak vagyunk j贸val stabilabbnak lefesteni az 茅lethelyzet眉nket, mint amilyen val贸j谩ban, 茅s ritk谩n tessz眉k ki ny铆ltan a k枚z枚ss茅gi oldalakra, hogy a PornHub-r贸l szoktunk esti mes茅t v谩lasztani magunknak. (脡n persze nem. De TI igen. H枚h.) Ugyanakkor ha egy f茅rfi a Google-ban r谩keres, hogyan tudn谩 枚nmag谩t, khm, a sz谩j谩val, khm, nem is folytatom, az nem felt茅tlen眉l azt jelenti, hogy komolyan foglalkoztatja a kivitelez茅s 鈥� lehet, csak olvasta Stephen-Davidowitzn谩l, hogy sokan ilyesmi ir谩nt 茅rdekl艖dnek, 茅s egyszer疟en meg枚li a k铆v谩ncsis谩g, ezt m茅gis ki k茅pes v茅ghez vinni. Sz贸val az, hogy ki hazudik 茅s ki mond igazat, bonyolult k茅rd茅s, legal谩bbis 贸vatosan kell teh谩t kezeln眉nk a szerz艖 azon 谩ll铆t谩s谩t, hogy az 艖 m贸dszere egyfajta 鈥瀒gazs谩gsz茅rum鈥�**.
Val贸j谩ban ez a k枚nyv himnusz a Big Data adatkutat贸ihoz. Seth Stephens-Davidowitz igazi k枚zponti 谩ll铆t谩sa (szerintem 茅s h谩la Istennek) nem a hazugs谩gokkal kapcsolatos, hanem azzal, hogy a Big Data olyan eszk枚z, ami a puszta m茅ret茅vel teljesen 谩talak铆tja a tudom谩nyos kutat谩st. Hiszen eddig az volt, hogy a t谩rsadalomtud贸soknak elk茅peszt艖en bonyolult 茅s k枚lts茅ges volt k铆s茅rletezni眉k 鈥� a k茅rd艖铆ves rendszerek, a csoportokat 茅s kontrollcsoportokat felhaszn谩l贸 kutat谩sok egyar谩nt hosszadalmas el艖k茅sz眉leteket ig茅nyeltek, r谩ad谩sul az eredm茅nyek gyakran ugyan煤gy k茅ts茅gbevonhat贸ak voltak, vagy nem 茅rt茅k meg a befektetett t艖k茅t. Ezzel szemben ma egy techc茅g naponta ak谩r t枚bb ezer 煤n 鈥濧/B tesztet***鈥� is lefuttathat kv谩zi automatikusan, minden probl茅ma n茅lk眉l, 茅s h谩la az internet elterjed茅s茅nek, a kutat贸k a Google t枚bb milli谩rdos adatb谩zis谩b贸l tudnak dolgozni, olyan inform谩ci贸kat h铆vva le a rendszerb艖l p谩r billenty疟 le眉t茅s茅vel (茅s n茅mi kreativit谩ssal), amir艖l p谩r 茅vtizede m茅g 谩lmodozni se mertek. Van egy hipot茅zis眉nk, miszerint Trump v谩laszt贸i rasszist谩bbak, mint a t枚bbiek? N茅zz眉k meg, a Google-ben hol 铆rt谩k be a 鈥瀗igger viccek鈥� kifejez茅st a keres艖ablakba, 茅s az 铆gy kapott t茅rk茅pet vess眉k 枚ssze a v谩laszt谩si t茅rk茅pekkel. Voil谩. 脡s hogy ezt meg tudjuk tenni, az forradalmas铆tja a t谩rsadalomtudom谩nyt, ami eddig puha volt, 谩m most - azzal, hogy ilyen szimpl谩n tudunk hipot茅ziseket ellen艖rizni - l茅nyegesen egzaktabb谩 v谩lik****. Persze 鈥� ezt a szerz艖 nem tagadja 鈥� ez semmik茅ppen sem jelenti azt, hogy a megszokott kiscsoportos k铆s茅rleteket 茅s felm茅r茅seket el lehet felejteni, puszt谩n azt, hogy a kett艖 egy眉tt haszn谩lva elk茅peszt艖en er艖s k枚vetkeztet茅seket fog eredm茅nyezni. 脡s ebben t枚k茅letesen egyet茅rtek vele.
脰sszess茅g茅ben ez a k枚nyv baromi informat铆v 茅s hat谩sos, ha azokat a passzusokat n茅zz眉k, amikor a titkolt, explicit el艖铆t茅letess茅gr艖l besz茅l, amelyek m茅g mindig hemzsegnek a nyugati civiliz谩ci贸 谩lcah谩l贸ja alatt. M谩r puszt谩n ez茅rt is 茅rdemes elolvasni. M谩sfel艖l viszont olyannyira k枚zpontba helyezi a szexualit谩s t茅mak枚r茅t, hogy azt m谩r neh茅z m谩snak 茅rtelmezni, mint vev艖csalogat谩snak. J贸, h谩t bevallom, ezek a r茅szek engem is sz贸rakoztattak, de az茅rt k枚zben t枚bbsz枚r elm茅l谩ztam azon, mennyire relev谩nsak az eff茅le inform谩ci贸k. Ha Seth Stephens-Davidowitz azt akarta bizony铆tani vel眉k, hogy t茅nyleg, de t茅nyleg mindenki hazudik, akkor jelentem, ez m谩r durv谩n 15-20 oldal ut谩n siker眉lt neki, a t枚bbi csak a tejsz铆nhab a gesztenyep眉r茅n. K眉l枚nben meg finom a tejsz铆nhab is. 脷gyhogy nem panaszkodom.
* Mondjuk ahhoz k茅pest, hogy a szerz艖 eredetileg a 鈥濵ekkora a p茅niszem?鈥� c铆mmel akarta piacra dobni ezt a term茅ket, a 鈥濵indenki hazudik鈥� 谩ll铆t谩s kifejezetten visszafogottnak t疟nik. Ugyanakkor 枚nmag谩ban jelzi, hogy Stephens-Davidowitz nem retten vissza egy kis marketingdinamit-haszn谩latt贸l.
** Jellemz艖 a m贸dszer korl谩tait illet艖en a p茅lda, amit a z谩rsz贸ban el艖cit谩l. Itt a szerz艖 hivatkozik egy matematikusra, aki abb贸l kiindulva, hogy a k枚nyvek els艖 fel茅b艖l t枚bbet id茅znek, mint a k枚nyvek m谩sodik fel茅b艖l, azt a k枚vetkeztet茅st vonta le, hogy az emberek kevesebb k枚nyvet olvasnak v茅gig, mint ahogy azt 谩ll铆tj谩k. Csin谩lt ilyen kis csecse statisztik谩t is a Big Data felhaszn谩l谩s谩val, amiben kisz谩molta, hogy az id茅zetek oldalsz谩moz谩s谩b贸l 铆t茅lve h谩ny sz谩zal茅k fejezte be v茅g眉l az adott m疟veket. (Piketty T艖k茅j茅t p茅ld谩ul 3%.) No most 茅n szint茅n gyakrabban id茅zek a k枚nyvek els艖 fel茅b艖l, mint a v茅g茅b艖l, m茅gpedig az茅rt, mert hajlamos vagyok az els艖 sz谩z oldalr贸l mindent ki铆rni, ami megtetszik, ut谩na viszont ellustulok, 茅s jobban megsz疟r枚m, mit 茅rdemes, mit nem. Lehet, a hipot茅zis ink谩bb ki枚tl艖je saj谩t olvas谩si szok谩sair贸l 谩rulkodik, mint az eny茅mr艖l.
*** Olyan tesztek ezek, amelyeket mondjuk a Google, az Amazon vagy a Facebook m谩r 茅vek 贸ta t枚meg茅vel futtat. P茅ld谩ul t枚bb ezer random kiv谩lasztott felhaszn谩l贸nak a k茅k egy bizonyos 谩rnyalat谩ban j枚n f枚l az oldal, m谩s felhaszn谩l贸knak meg egy m谩sik 谩rnyalattal. Amelyikre t枚bb kattint谩st gener谩l, az lesz az optim谩lis sz铆n.
**** 脡rdekes, hogy mindek枚zben a term茅szettudom谩nyok mintha vesz铆ten茅nek egzakts谩gukb贸l. Legal谩bbis a kvantumfizika vagy a h煤relm茅let kezd annyira elm茅leti lenni, ami k铆s茅rleti 煤ton m谩r nem is igazolhat贸.
February 22, 2020
At 58%, I give up. DNF.
I've seldom read anything that contained so many individually interesting (if shallow) sentences and still bored the hell out of me. I'm also tired of reading about the author's infatuations with baseball, Google, and porn. I am counting this book as read, however, because I should get some small (if valueless) reward for the time I lost reading it.
Some random, non-linear thoughts because I'm not interested enough in the book to try harder at this point:
1. The author worked for Google. Apparently, he's still smitten by them and thinks their massive collection of data on users is the most wondrous gift to humans ever. Of course he does. His degree and career depend on it.
I am of the opinion that the most, and perhaps the only, honest thing Google has ever done is when they got rid of their "Don't be evil" corporate slogan. Google is kind of like Walmart to me. I don't like it, trust it, or believe anything it claims and use it as little as possible.
2. The fact that the government turned over every American's tax data to whatever researchers want to dig through it--and in such detail that they can trace income changes for every address a single individual has reported from--is worrisome. "Oh, but the individuals weren't identified." Bullshit. Maybe not by name, but there is enough info there to cross-match with other data and individually identify people. That every search you've every made is time/location stamped and available to whoever wants it is just effing creepy. While we all should know by now that we're just products to be data-mined for profit, seeing a time-stamped list of an individual's exact searches over a 24-hour period is disconcerting.
3. If this guy is right, I've been using search engines for all the wrong things. (Read that in snark font.) Do people really do Google searches for "Why are Jews cheap?" or "Is my daughter ugly?" or "Am I gay?" Gee, all this time, I've been using searches for things like "What is the capital of Latvia" or "What is the formula to calculating amortization?" Every now and then I sink to searching for David Bowie music videos. I think I once stooped to "Why the hell are the Kardashians famous?" Supposedly women everywhere are Googling "Does my vagina smell?" and "Is my husband cheating"? Well, honey, if you have to ask....
4. Data for data sake serves little purpose. It just makes for more noise, not more clarity.
5 . Apparently the author believes that the way to keep readers' interest is to use the most sensationalist examples he could come up with. Throw around a lot of racist terms and references to sex kinks or insecurities. When all else fails, talk baseball and throw in some frat boy humor. I get it: the porn industry drives nearly every internet innovation, from web design to security and data collection. Fine. But the tenth or 15th time he refers to women being worried about vaginal odor or how common Pornhub searches for incest videos are, it comes off as an attempt to be provocative rather than informative. For a chapter or two when that content is relevant to the topic, fine. Every single chapter? Boring. And really creepy after a while.
6. If there is any discussion about the ethics of data mining in this thing, it's so far buried in the back that I didn't get to it. If there is substantive material on statistical relevance, hypothesis testing or phantom populations, it's buried in the back.
He's the kid who walks in with his bright, shiny, non-traditional method and basically says he's right and everyone else who ever studied data is wrong.
(And maybe, if as the author claims, the last chapters of books don't get read as much as the beginning ones, it's because books are getting less and less informative and/or credible.)
I've seldom read anything that contained so many individually interesting (if shallow) sentences and still bored the hell out of me. I'm also tired of reading about the author's infatuations with baseball, Google, and porn. I am counting this book as read, however, because I should get some small (if valueless) reward for the time I lost reading it.
Some random, non-linear thoughts because I'm not interested enough in the book to try harder at this point:
1. The author worked for Google. Apparently, he's still smitten by them and thinks their massive collection of data on users is the most wondrous gift to humans ever. Of course he does. His degree and career depend on it.
I am of the opinion that the most, and perhaps the only, honest thing Google has ever done is when they got rid of their "Don't be evil" corporate slogan. Google is kind of like Walmart to me. I don't like it, trust it, or believe anything it claims and use it as little as possible.
2. The fact that the government turned over every American's tax data to whatever researchers want to dig through it--and in such detail that they can trace income changes for every address a single individual has reported from--is worrisome. "Oh, but the individuals weren't identified." Bullshit. Maybe not by name, but there is enough info there to cross-match with other data and individually identify people. That every search you've every made is time/location stamped and available to whoever wants it is just effing creepy. While we all should know by now that we're just products to be data-mined for profit, seeing a time-stamped list of an individual's exact searches over a 24-hour period is disconcerting.
3. If this guy is right, I've been using search engines for all the wrong things. (Read that in snark font.) Do people really do Google searches for "Why are Jews cheap?" or "Is my daughter ugly?" or "Am I gay?" Gee, all this time, I've been using searches for things like "What is the capital of Latvia" or "What is the formula to calculating amortization?" Every now and then I sink to searching for David Bowie music videos. I think I once stooped to "Why the hell are the Kardashians famous?" Supposedly women everywhere are Googling "Does my vagina smell?" and "Is my husband cheating"? Well, honey, if you have to ask....
4. Data for data sake serves little purpose. It just makes for more noise, not more clarity.
5 . Apparently the author believes that the way to keep readers' interest is to use the most sensationalist examples he could come up with. Throw around a lot of racist terms and references to sex kinks or insecurities. When all else fails, talk baseball and throw in some frat boy humor. I get it: the porn industry drives nearly every internet innovation, from web design to security and data collection. Fine. But the tenth or 15th time he refers to women being worried about vaginal odor or how common Pornhub searches for incest videos are, it comes off as an attempt to be provocative rather than informative. For a chapter or two when that content is relevant to the topic, fine. Every single chapter? Boring. And really creepy after a while.
6. If there is any discussion about the ethics of data mining in this thing, it's so far buried in the back that I didn't get to it. If there is substantive material on statistical relevance, hypothesis testing or phantom populations, it's buried in the back.
He's the kid who walks in with his bright, shiny, non-traditional method and basically says he's right and everyone else who ever studied data is wrong.
(And maybe, if as the author claims, the last chapters of books don't get read as much as the beginning ones, it's because books are getting less and less informative and/or credible.)
November 3, 2019
讴鬲丕亘 賮賵賯 丕賱毓丕丿賴 丕蹖 亘賵丿 賵 丿乇 丕氐賱 賯丿乇鬲 丿丕丿賴 賴丕 乇賵 丿乇 毓氐乇 賲丿乇賳 賳卮賵賳 賲蹖丿丕丿.
鬲賯乇蹖亘丕 賲蹖卮賴 噩賵丕亘 賴賲賴 爻賵丕賱丕鬲 乇賵 亘丕 鬲丨賱蹖賱 丿丕丿賴 賴丕 倬蹖丿丕 讴乇丿.
亘毓丿 丕夭 禺賵賳丿賳 丕蹖賳 讴鬲丕亘 亘賴 倬蹖卮 亘蹖賳蹖 倬乇賵賮爻賵乇 賴乇丕乇蹖 讴賴 丿乇 讴鬲丕亘 丕賳爻丕賳 禺丿丕诏賵賳賴 賳诏乇卮 丨丕讴賲 丌蹖賳丿賴 噩賴丕賳 乇賵 丿丕丿賴 亘丕賵乇蹖 禺賵賳丿賴 亘賵丿 亘蹖卮鬲乇 丕蹖賲丕賳 丌賵乇丿賲.
丕賲蹖丿賵丕乇賲 丌賲賵夭卮 賲鬲禺氐氐蹖賳 丿丕丿賴 丿乇 讴卮賵乇 乇賵賳賯 亘诏蹖乇賴 讴賴 亘賴 賳馗乇 賲蹖丕丿 賴賲蹖賳 丕賱丕賳卮 賴賲 賳蹖丕夭 賲亘乇賲 噩丕賲毓賴 丕爻鬲.
鬲賯乇蹖亘丕 賲蹖卮賴 噩賵丕亘 賴賲賴 爻賵丕賱丕鬲 乇賵 亘丕 鬲丨賱蹖賱 丿丕丿賴 賴丕 倬蹖丿丕 讴乇丿.
亘毓丿 丕夭 禺賵賳丿賳 丕蹖賳 讴鬲丕亘 亘賴 倬蹖卮 亘蹖賳蹖 倬乇賵賮爻賵乇 賴乇丕乇蹖 讴賴 丿乇 讴鬲丕亘 丕賳爻丕賳 禺丿丕诏賵賳賴 賳诏乇卮 丨丕讴賲 丌蹖賳丿賴 噩賴丕賳 乇賵 丿丕丿賴 亘丕賵乇蹖 禺賵賳丿賴 亘賵丿 亘蹖卮鬲乇 丕蹖賲丕賳 丌賵乇丿賲.
丕賲蹖丿賵丕乇賲 丌賲賵夭卮 賲鬲禺氐氐蹖賳 丿丕丿賴 丿乇 讴卮賵乇 乇賵賳賯 亘诏蹖乇賴 讴賴 亘賴 賳馗乇 賲蹖丕丿 賴賲蹖賳 丕賱丕賳卮 賴賲 賳蹖丕夭 賲亘乇賲 噩丕賲毓賴 丕爻鬲.
August 19, 2021
蹖讴 禺亘乇 丕夭 乇賵夭賴丕蹖 丿賵乇--> 丌倬丿蹖鬲 噩丿蹖丿 賵丕鬲爻鈥屫з� 讴丕乇亘乇丕賳 乇丕 賲噩亘賵乇 賲蹖鈥屭┵嗀� 亘乇丕蹖 丕丿丕賲賴 丕爻鬲賮丕丿賴 丕夭 丕蹖賳 丕倬賱蹖讴蹖卮賳 蹖丕 丕胤賱丕毓丕鬲 禺賵丿 乇丕 亘丕 賮蹖爻鈥屫ㄙ堏� 亘賴 丕卮鬲乇丕讴 亘诏匕丕乇賳丿 蹖丕 丿爻鬲乇爻蹖 亘賴 賵丕鬲爻鈥屫з� 乇丕 丕夭 丿爻鬲 賲蹖鈥屫囐嗀�. 賵丕鬲爻鈥屫з� 亘丕 丌倬丿蹖鬲 噩丿蹖丿 讴丕乇亘乇丕賳卮 乇丕 賲噩亘賵乇 賲蹖鈥屭┵嗀� 鬲丕 亘丕 賯丕賳賵賳 噩丿蹖丿 丨賮丕馗鬲 丕夭 丕胤賱丕毓丕鬲 卮禺氐蹖 賲賵丕賮賯鬲 讴賳賳丿 賵 丿乇 睾蹖乇 丕蹖賳 氐賵乇鬲 丿爻鬲乇爻蹖鈥屫簇з� 亘賴 丕倬 乇丕 丕夭 丿爻鬲 賲蹖鈥屫囐嗀�. 賲賵丕賮賯鬲 讴乇丿賳 亘丕 賯賵丕賳蹖賳 噩丿蹖丿 賵丕鬲爻鈥屫з� 亘賴 賲毓賳丕蹖 丌賳 丕爻鬲 讴賴 丕胤賱丕毓丕鬲 禺氐賵氐蹖 讴丕乇亘乇 丕夭 噩賲賱賴 卮賲丕乇賴 賲賵亘丕蹖賱 賵蹖 亘丕 賮蹖爻鈥屫ㄙ堏� (賲丕賱讴 倬蹖丕賲 乇爻丕賳) 亘賴 丕卮鬲乇丕讴 诏匕丕卮鬲賴 賲蹖鈥屫促堌�. 鬲賲丕賲 讴丕乇亘乇丕賳 丕蹖賳 丕倬 亘丕蹖丿 鬲丕 侉 賮賵乇蹖賴 佗贍佗佟 賲蹖賱丕丿蹖 亘丕 賯賵丕賳蹖賳 噩丿蹖丿 賲賵丕賮賯鬲 讴賳賳丿
丕蹖賳 禺亘乇 乇賵 蹖丕丿鬲賵賳賴責 趩賳丿 賵賯鬲 倬蹖卮 丿乇 诏乇賵賴锟斤拷賴丕貙 丕蹖賳 丌倬丿蹖鬲 賵丕鬲爻丕倬 噩賳噩丕賱蹖 卮丿賴 亘賵丿 賵 賲賱鬲 丕夭 賴賲 賲蹖鈥屬矩必驰屫� 趩乇丕責 丕胤賱丕毓丕鬲 賲丕 亘賴 趩賴 丿乇丿卮賵賳 賲蹖禺賵乇賴責 趩蹖 讴丕乇 賲蹖鈥屫堌з� 亘讴賳賳 亘丕 丕胤賱丕毓丕鬲 賲丕責 亘賴 趩蹖 賲蹖鈥屫必迟嗀� 賵 禺賱丕氐賴 讴賱蹖 爻賵丕賱 丕蹖賳 趩賳蹖賳蹖 讴賴 禺蹖賱蹖 丕夭 丿賵爻鬲丕賳 乇賵 丿乇诏蹖乇 讴乇丿賴 亘賵丿!
亘乇丕蹖 噩賵丕亘 丿丕丿賳 亘賴 丕蹖賳 爻賵丕賱丕鬲 亘賴 氐賵乇鬲 毓丕賲 賵 亘丿賵賳 賴蹖趩 亘丨孬 鬲禺氐氐蹖貙 賯胤毓丕 丕蹖賳 讴鬲丕亘 乇賵 鬲賵氐蹖賴 賲蹖鈥屭┵嗁� 亘賴 禺賵賳丿賳! 賳賲賵賳賴鈥屬囏й� 讴賵趩蹖讴蹖 丕夭 鬲丨賱蹖賱 丿丕丿賴鈥屬囏й� 孬亘鬲 卮丿賴 丕夭 讴丕乇亘乇丕賳 乇賵 丿丕乇賴! 丕夭 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 亘诏蹖乇蹖賳 鬲丕 丕胤賱丕毓丕鬲 爻乇趩 诏賵诏賱 賵 睾蹖乇賴!
賯胤毓丕 禺蹖賱蹖 丕夭 爻賵丕賱丕鬲 丕蹖賳 趩賳蹖賳蹖 乇賵 噩賵丕亘 賲蹖丿賴 賵 匕賴賳 丿賵爻鬲丕賳蹖 讴賴 丕夭 丿賳蹖丕蹖 丿蹖鬲丕爻鬲 賵 亘蹖诏鈥屫屫� 亘賴 丿賵乇賳 乇賵 乇賵卮賳 賲蹖讴賳賴 ! 賴乇趩賳丿 亘賴 賯賵賱 賳賵蹖爻賳丿賴貙 丕诏乇 50 氐賮丨賴 乇賵 禺賵賳丿蹖 賵 丿蹖诏賴 丨賵氐賱賴鈥屰� 丕丿丕賲卮 乇賵 賳丿丕卮鬲蹖 讴丕賲賱丕 胤亘蹖毓蹖 賵 賲卮讴賱蹖 賳丿丕乇賴! 丕蹖賳 蹖毓賳蹖 賳賵蹖爻賳丿賴 賲蹖丿賵賳賴 乇賵丿賴 丿乇丕夭蹖 讴乇丿賴 賵 卮丕蹖丿 蹖匕乇賴 賲鬲賳 乇賵 亘賴 噩丕賴丕蹖 丨賵氐賱賴 爻乇 亘乇 讴卮蹖丿賴 賵賱蹖 丿乇 讴賱 亘乇丕蹖 賲賳 鬲丕 丌禺乇蹖賳 禺胤卮 丕乇夭卮賲賳丿 亘賵丿貙 趩賵賳 丨爻 賲蹖讴乇丿賲 亘丕 讴鬲丕亘蹖 丿乇诏蹖乇賲 讴賴 賳賵蹖爻賳丿賴 亘賴 禺丕賱賴 夭賳讴 亘丕夭蹖 賲丿乇賳 毓賱丕賯賴鈥屬呝嗀� 賵 亘賴 賲賳 禺賵丕賳賳丿賴鈥屰� 賮囟賵賱卮 亘丿賵賳 夭丨賲鬲貙 丕胤賱丕毓丕鬲蹖 讴賴 丕夭 丿蹖鬲丕爻鬲鈥屬囏й� 賲禺鬲賱賮 丿乇 丌賵乇丿賴 乇賵 乇丕丨鬲 讴賮 丿爻鬲賲 賲蹖匕丕乇賴!
亘賴 胤賵乇 賲孬丕賱 卮賲丕 賮讴 讴賳 蹖賴 讴丕爻賴 鬲禺賲賴 亘乇 賲蹖丿丕乇蹖 賲蹖卮蹖賳蹖 賵 蹖讴 賲丨賯賯 賲蹖丕丿 乇賵亘乇賵鬲 賵 亘乇丕鬲 鬲毓乇蹖賮 賲蹖讴賳賴 讴賴貙 賲乇丿賲 賴賳丿 丿乇 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 賴丕亘 亘蹖卮鬲乇蹖賳 爻乇趩卮賵賳 趩蹖 亘賵丿賴責! 蹖丕 丕蹖賳讴賴 賵賯鬲蹖 丿乇 賮賱丕賳 丕蹖丕賱鬲 爻賯胤 噩賳蹖賳 乇賵 賲賲賳賵毓 讴乇丿賳 亘蹖鬲卮乇蹖賳 爻乇趩 賲賱鬲 亘賴 乇賵卮鈥屬囏й� 爻賯胤 噩賳蹖賳 丕夭 胤乇蹖賯 賵蹖鬲丕賲蹖賳 爻蹖貙 噩毓賮乇蹖貙 趩賵亘 賱亘丕爻 爻蹖賲蹖 亘賵丿賴 賵 亘毓丿 鬲賵 丨丕賱鬲 亘賴賲 賲蹖禺賵乇賴 賵 賲蹖诏蹖 禺丕讴 亘賴 爻乇鬲 亘丕 丕蹖賳 爻乇趩丕鬲 賵 亘丕夭 賲蹖倬乇爻蹖 丿蹖诏賴 趩蹖 賮賴賲蹖丿蹖 賵 丕丿丕賲賴 賲蹖丿蹖 亘賴 卮賳蹖丿賳 賵 禺賵賳丿賳 !!!!
賲蹖诏賲 讴鬲丕亘 亘乇丕蹖 賲賳 禺賵丕賳賳丿賴鈥屰� 賮賵囟賵賱 噩匕丕亘 亘賵丿貙 亘乇丕 丕蹖賳 趩蹖夭丕卮 賲蹖诏賲!
丨丕賱丕 賲賵囟賵毓 丕蹖賳讴賴 趩乇丕 丕蹖賳 丕爻賲 乇賵 亘乇丕蹖 讴鬲丕亘 诏匕丕卮鬲賳責
卮丕蹖丿 亘丕乇夭鬲乇蹖賳 丿賱蹖賱蹖 讴賴 賳賵蹖爻賳丿賴 亘乇丕卮 賲蹖丕乇賴 丕蹖賳讴賴貙 賲乇丿賲 丿乇 馗丕賴乇 蹖賴 趩蹖夭蹖 賲蹖诏賳 賵賱蹖 丿乇 禺賮丕 亘丕 讴賱蹖丿 讴蹖亘賵乇丿賴丕卮賵賳 睾蹖乇 丕夭 丕賵賳趩賴 讴賴 诏賮鬲賳 乇賵 爻乇趩 賲蹖讴賳賳! 賵 丕卮丕乇賴 賲蹖讴賳賴 讴賴 賳亘蹖賳蹖賳 賲賱鬲 趩蹖 賲蹖诏賳 亘賱讴賴 亘亘蹖賳蹖賳 賲賱鬲 趩蹖 丕賳噩丕賲 賲蹖丿賳 賵 亘丕 讴賱蹖 鬲丨賱蹖賱鈥屬囏й� 丿丕丿賴鈥屫й� 孬亘鬲 亘乇 丕丿毓丕蹖 丕蹖賳 賲賵囟賵毓 乇賵 丿丕乇賴!
禺賱丕氐賴 讴賴 讴鬲丕亘 亘丕 丕蹖賳讴賴 亘乇丕蹖 賲賳 亘爻蹖丕乇 噩匕丕亘 亘賵丿 賵賱蹖 賲賲讴賳賴 亘乇丕蹖 卮賲丕 丨賵氐賱賴 爻乇 亘乇 亘丕卮賴貙 倬爻 丌賲丕丿诏蹖 丕蹖賳 倬丕乇丕丿賵讴爻 乇賵 賯亘賱丕 丕夭 禺賵賳丿賳卮 丿丕卮鬲賴 亘丕卮蹖賳.
丕蹖賳 禺亘乇 乇賵 蹖丕丿鬲賵賳賴責 趩賳丿 賵賯鬲 倬蹖卮 丿乇 诏乇賵賴锟斤拷賴丕貙 丕蹖賳 丌倬丿蹖鬲 賵丕鬲爻丕倬 噩賳噩丕賱蹖 卮丿賴 亘賵丿 賵 賲賱鬲 丕夭 賴賲 賲蹖鈥屬矩必驰屫� 趩乇丕責 丕胤賱丕毓丕鬲 賲丕 亘賴 趩賴 丿乇丿卮賵賳 賲蹖禺賵乇賴責 趩蹖 讴丕乇 賲蹖鈥屫堌з� 亘讴賳賳 亘丕 丕胤賱丕毓丕鬲 賲丕責 亘賴 趩蹖 賲蹖鈥屫必迟嗀� 賵 禺賱丕氐賴 讴賱蹖 爻賵丕賱 丕蹖賳 趩賳蹖賳蹖 讴賴 禺蹖賱蹖 丕夭 丿賵爻鬲丕賳 乇賵 丿乇诏蹖乇 讴乇丿賴 亘賵丿!
亘乇丕蹖 噩賵丕亘 丿丕丿賳 亘賴 丕蹖賳 爻賵丕賱丕鬲 亘賴 氐賵乇鬲 毓丕賲 賵 亘丿賵賳 賴蹖趩 亘丨孬 鬲禺氐氐蹖貙 賯胤毓丕 丕蹖賳 讴鬲丕亘 乇賵 鬲賵氐蹖賴 賲蹖鈥屭┵嗁� 亘賴 禺賵賳丿賳! 賳賲賵賳賴鈥屬囏й� 讴賵趩蹖讴蹖 丕夭 鬲丨賱蹖賱 丿丕丿賴鈥屬囏й� 孬亘鬲 卮丿賴 丕夭 讴丕乇亘乇丕賳 乇賵 丿丕乇賴! 丕夭 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 亘诏蹖乇蹖賳 鬲丕 丕胤賱丕毓丕鬲 爻乇趩 诏賵诏賱 賵 睾蹖乇賴!
賯胤毓丕 禺蹖賱蹖 丕夭 爻賵丕賱丕鬲 丕蹖賳 趩賳蹖賳蹖 乇賵 噩賵丕亘 賲蹖丿賴 賵 匕賴賳 丿賵爻鬲丕賳蹖 讴賴 丕夭 丿賳蹖丕蹖 丿蹖鬲丕爻鬲 賵 亘蹖诏鈥屫屫� 亘賴 丿賵乇賳 乇賵 乇賵卮賳 賲蹖讴賳賴 ! 賴乇趩賳丿 亘賴 賯賵賱 賳賵蹖爻賳丿賴貙 丕诏乇 50 氐賮丨賴 乇賵 禺賵賳丿蹖 賵 丿蹖诏賴 丨賵氐賱賴鈥屰� 丕丿丕賲卮 乇賵 賳丿丕卮鬲蹖 讴丕賲賱丕 胤亘蹖毓蹖 賵 賲卮讴賱蹖 賳丿丕乇賴! 丕蹖賳 蹖毓賳蹖 賳賵蹖爻賳丿賴 賲蹖丿賵賳賴 乇賵丿賴 丿乇丕夭蹖 讴乇丿賴 賵 卮丕蹖丿 蹖匕乇賴 賲鬲賳 乇賵 亘賴 噩丕賴丕蹖 丨賵氐賱賴 爻乇 亘乇 讴卮蹖丿賴 賵賱蹖 丿乇 讴賱 亘乇丕蹖 賲賳 鬲丕 丌禺乇蹖賳 禺胤卮 丕乇夭卮賲賳丿 亘賵丿貙 趩賵賳 丨爻 賲蹖讴乇丿賲 亘丕 讴鬲丕亘蹖 丿乇诏蹖乇賲 讴賴 賳賵蹖爻賳丿賴 亘賴 禺丕賱賴 夭賳讴 亘丕夭蹖 賲丿乇賳 毓賱丕賯賴鈥屬呝嗀� 賵 亘賴 賲賳 禺賵丕賳賳丿賴鈥屰� 賮囟賵賱卮 亘丿賵賳 夭丨賲鬲貙 丕胤賱丕毓丕鬲蹖 讴賴 丕夭 丿蹖鬲丕爻鬲鈥屬囏й� 賲禺鬲賱賮 丿乇 丌賵乇丿賴 乇賵 乇丕丨鬲 讴賮 丿爻鬲賲 賲蹖匕丕乇賴!
亘賴 胤賵乇 賲孬丕賱 卮賲丕 賮讴 讴賳 蹖賴 讴丕爻賴 鬲禺賲賴 亘乇 賲蹖丿丕乇蹖 賲蹖卮蹖賳蹖 賵 蹖讴 賲丨賯賯 賲蹖丕丿 乇賵亘乇賵鬲 賵 亘乇丕鬲 鬲毓乇蹖賮 賲蹖讴賳賴 讴賴貙 賲乇丿賲 賴賳丿 丿乇 爻丕蹖鬲鈥屬囏й� 倬賵乇賳 賴丕亘 亘蹖卮鬲乇蹖賳 爻乇趩卮賵賳 趩蹖 亘賵丿賴責! 蹖丕 丕蹖賳讴賴 賵賯鬲蹖 丿乇 賮賱丕賳 丕蹖丕賱鬲 爻賯胤 噩賳蹖賳 乇賵 賲賲賳賵毓 讴乇丿賳 亘蹖鬲卮乇蹖賳 爻乇趩 賲賱鬲 亘賴 乇賵卮鈥屬囏й� 爻賯胤 噩賳蹖賳 丕夭 胤乇蹖賯 賵蹖鬲丕賲蹖賳 爻蹖貙 噩毓賮乇蹖貙 趩賵亘 賱亘丕爻 爻蹖賲蹖 亘賵丿賴 賵 亘毓丿 鬲賵 丨丕賱鬲 亘賴賲 賲蹖禺賵乇賴 賵 賲蹖诏蹖 禺丕讴 亘賴 爻乇鬲 亘丕 丕蹖賳 爻乇趩丕鬲 賵 亘丕夭 賲蹖倬乇爻蹖 丿蹖诏賴 趩蹖 賮賴賲蹖丿蹖 賵 丕丿丕賲賴 賲蹖丿蹖 亘賴 卮賳蹖丿賳 賵 禺賵賳丿賳 !!!!
賲蹖诏賲 讴鬲丕亘 亘乇丕蹖 賲賳 禺賵丕賳賳丿賴鈥屰� 賮賵囟賵賱 噩匕丕亘 亘賵丿貙 亘乇丕 丕蹖賳 趩蹖夭丕卮 賲蹖诏賲!
丨丕賱丕 賲賵囟賵毓 丕蹖賳讴賴 趩乇丕 丕蹖賳 丕爻賲 乇賵 亘乇丕蹖 讴鬲丕亘 诏匕丕卮鬲賳責
卮丕蹖丿 亘丕乇夭鬲乇蹖賳 丿賱蹖賱蹖 讴賴 賳賵蹖爻賳丿賴 亘乇丕卮 賲蹖丕乇賴 丕蹖賳讴賴貙 賲乇丿賲 丿乇 馗丕賴乇 蹖賴 趩蹖夭蹖 賲蹖诏賳 賵賱蹖 丿乇 禺賮丕 亘丕 讴賱蹖丿 讴蹖亘賵乇丿賴丕卮賵賳 睾蹖乇 丕夭 丕賵賳趩賴 讴賴 诏賮鬲賳 乇賵 爻乇趩 賲蹖讴賳賳! 賵 丕卮丕乇賴 賲蹖讴賳賴 讴賴 賳亘蹖賳蹖賳 賲賱鬲 趩蹖 賲蹖诏賳 亘賱讴賴 亘亘蹖賳蹖賳 賲賱鬲 趩蹖 丕賳噩丕賲 賲蹖丿賳 賵 亘丕 讴賱蹖 鬲丨賱蹖賱鈥屬囏й� 丿丕丿賴鈥屫й� 孬亘鬲 亘乇 丕丿毓丕蹖 丕蹖賳 賲賵囟賵毓 乇賵 丿丕乇賴!
禺賱丕氐賴 讴賴 讴鬲丕亘 亘丕 丕蹖賳讴賴 亘乇丕蹖 賲賳 亘爻蹖丕乇 噩匕丕亘 亘賵丿 賵賱蹖 賲賲讴賳賴 亘乇丕蹖 卮賲丕 丨賵氐賱賴 爻乇 亘乇 亘丕卮賴貙 倬爻 丌賲丕丿诏蹖 丕蹖賳 倬丕乇丕丿賵讴爻 乇賵 賯亘賱丕 丕夭 禺賵賳丿賳卮 丿丕卮鬲賴 亘丕卮蹖賳.
June 4, 2017
I couldn't even make it through the introduction. This is a perfect example of starting with a conclusion and then finding the data to support your conclusion. All a search shows you is the number of times the word or phrase is searched. It does not show intention. It does not show the number of times a certain person searches for the same word or phrase. The author makes a lot of assumptions based on his own presuppositions. I thought this would be a fun read, but I have no intention of slogging through a heavily biased account of his own ideas. Data is just that-data. It is one piece of a puzzle. It is not the puzzle.
January 22, 2020
禺賵賳丿賳 賵 卮乇賵毓 丕蹖賳 讴鬲丕亘 賴賲夭賲丕賳 卮丿 亘丕 賵乇賵丿 賲賳 亘賴 丨賵夭賴 亘爻蹖丕乇 亘夭乇诏 丿蹖鬲丕 丿乇 蹖讴 卮乇讴鬲 丕爻鬲丕乇鬲丕倬蹖
賲胤丕賱亘 讴鬲丕亘 亘丕 賵噩賵丿 丕蹖賳讴賴 丕夭 丨賵夭賴 毓賱賲 賵 丿丕賳卮 亘蹖诏 丿蹖鬲丕 亘賴卮 賳诏丕賴 賲蹖卮賴 丕賲丕 禺蹖賱蹖 禺蹖賱蹖 賴蹖噩丕賳 丕賳诏蹖夭賴 .. 丕诏乇 賲蹖禺賵丕賴蹖丿 亘丿賵賳蹖丿 讴賱丕賳 丿丕丿賴 趩蹖 賴爻鬲 責 趩賴 丕胤賱丕毓丕鬲 噩丕賱亘蹖 賲蹖卮賴 丕夭卮 倬蹖丿丕 讴乇丿責 賵 丕蹖賳讴賴 丿乇 丿賳蹖丕蹖 丨丕囟乇 趩賯丿乇 賮囟丕蹖 賲噩丕夭蹖 賳卮丕賳 丿賴賳丿賴 乇賵丨蹖丕鬲貙 禺賱賯蹖丕鬲 賵 鬲賮讴乇丕鬲 賲丕 賴爻鬲賳丿 丨鬲賲丕 丕蹖賳 讴鬲丕亘 乇賵 亘禺賵賳蹖丿. 丿蹖丿 賴賲賴 噩丕賳亘賴 丕蹖 丕蹖賳 鬲丨賱蹖賱诏乇 丿丕丿賴 丿丕乇賴 (丿乇 賵丕賯毓 賴賲賴 鬲丨賱蹖賱 诏乇賴丕 亘賴 賳馗乇賲 亘賴 賲乇賵乇 丿乇 賴賲賴 噩賴丕鬲 夭賳丿诏蹖 丿趩丕乇 丕蹖賳 丿蹖丿 賴賲賴 噩丕賳亘賴 賳诏乇 賲蹖卮賴
禺賱丕氐賴 讴賴 禺賵賳丿賳卮 亘乇丕蹖 賲賳 丿賳蹖丕蹖蹖 丕夭 卮诏賮鬲蹖 丿丕卮鬲 賵 賲賳賵 亘蹖卮鬲乇 亘賴 丕蹖賳 丨賵夭賴 毓賱丕賯賴 賲賳丿 讴乇丿.
賲胤丕賱亘 讴鬲丕亘 亘丕 賵噩賵丿 丕蹖賳讴賴 丕夭 丨賵夭賴 毓賱賲 賵 丿丕賳卮 亘蹖诏 丿蹖鬲丕 亘賴卮 賳诏丕賴 賲蹖卮賴 丕賲丕 禺蹖賱蹖 禺蹖賱蹖 賴蹖噩丕賳 丕賳诏蹖夭賴 .. 丕诏乇 賲蹖禺賵丕賴蹖丿 亘丿賵賳蹖丿 讴賱丕賳 丿丕丿賴 趩蹖 賴爻鬲 責 趩賴 丕胤賱丕毓丕鬲 噩丕賱亘蹖 賲蹖卮賴 丕夭卮 倬蹖丿丕 讴乇丿責 賵 丕蹖賳讴賴 丿乇 丿賳蹖丕蹖 丨丕囟乇 趩賯丿乇 賮囟丕蹖 賲噩丕夭蹖 賳卮丕賳 丿賴賳丿賴 乇賵丨蹖丕鬲貙 禺賱賯蹖丕鬲 賵 鬲賮讴乇丕鬲 賲丕 賴爻鬲賳丿 丨鬲賲丕 丕蹖賳 讴鬲丕亘 乇賵 亘禺賵賳蹖丿. 丿蹖丿 賴賲賴 噩丕賳亘賴 丕蹖 丕蹖賳 鬲丨賱蹖賱诏乇 丿丕丿賴 丿丕乇賴 (丿乇 賵丕賯毓 賴賲賴 鬲丨賱蹖賱 诏乇賴丕 亘賴 賳馗乇賲 亘賴 賲乇賵乇 丿乇 賴賲賴 噩賴丕鬲 夭賳丿诏蹖 丿趩丕乇 丕蹖賳 丿蹖丿 賴賲賴 噩丕賳亘賴 賳诏乇 賲蹖卮賴
禺賱丕氐賴 讴賴 禺賵賳丿賳卮 亘乇丕蹖 賲賳 丿賳蹖丕蹖蹖 丕夭 卮诏賮鬲蹖 丿丕卮鬲 賵 賲賳賵 亘蹖卮鬲乇 亘賴 丕蹖賳 丨賵夭賴 毓賱丕賯賴 賲賳丿 讴乇丿.
February 17, 2024
蹖讴 讴鬲丕亘 毓丕賱蹖 丿乇亘丕乇賴 丿丕丿賴 讴丕賵蹖 賲賵囟賵毓丕鬲 丿丕睾 賵 倬乇丨丕卮蹖賴 讴賴 賴賲 夭亘丕賳 爻丕丿賴 賵 夭蹖亘丕蹖蹖 丿丕乇賴 賵 賴賲 賲賲讴賳賴 卮賲丕乇賵 毓丕卮賯 毓賱賲 丿丕丿賴 亘讴賳賴
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