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Zusammenfassung von Everybody Lies

Name: Everybody Lies
Rating: 4.1 (118 reviews)
Author: Seth Stephens-Davidowitz

Seth Stephens-Davidowitz

Big Data, New Data and What the Internet Can Tell Us About Who We Really Are

4.1 (118 Bewertungen)

18 Min.

Kurz zusammengefasst

Everybody Lies by Seth Stephens-Davidowitz is a fascinating exploration of the insights we can gain from big data. Based on Google searches and anonymous online behavior, the book challenges our assumptions about human nature and exposes the truth about what people really think and do.

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Das Internet Big Data

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What’s in it for me? Find out what’s hiding behind the big data curtain.

Let’s face it – no matter how true to ourselves and those around us we claim to be, to some extent or another, we still lie. Whether it’s inflating our benevolent characteristics in a survey about our lifestyle or not telling the full story about the sorts of weird stuff we do when no one is looking, everybody practices a bit of deceit in his day-to-day life.

But with the increasingly vast amounts of data that are collected through, for instance, Google searches, we can go beneath the surface and see the truth.

With such an impossibly large accumulation of data on countless aspects of human existence, a pool of information collectively known as big data, it is now possible to analyze revealing patterns in our behavior and identify preferences that we never knew about before.

In these blinks, you’ll learn about what big data has to offer, from reporting on the state of our health, to revealing strange human quirks, to helping conduct endless randomized, controlled experiments.

You’ll also discover

what kinky preference some women have that involves apples;
how big data reveals the cities in which the American dream lives on; and
whether big data should be used to prevent suicide.

Data science is more intuitive than you think.

You’ve heard the term, but what exactly is big data?

The clue is in the name. Big data refers to an immense volume of data. A volume that’s so vast, in fact, that the human mind can hardly comprehend it. In other word, big data is data for which computational power is required to recognize patterns. Paradoxically, however, despite its remarkable scale, data science has an intuitive aspect to it. After all, if you think about it, we’re all data scientists in a way.

The author points to his grandmother. One Thanksgiving, she started suggesting to the author what his ideal partner should be like – at least as far as she was concerned! The partner needed to be clever, nice, funny, sociable and pretty (though no supermodels needed apply).

At 88, his grandma had seen plenty of relationships come and go. She was using years of information and data gathering to articulate the characteristics she saw as essential in successful relationships. She was utilizing information to spot patterns and predict how certain variables would impact one another – just as a data scientist would.

However, although data science is an intuitive process, intuition itself isn’t actually science. That’s why utilizing gathered data correctly is essential to refining one’s worldview. Data provides us with the material to confirm or rebut our initial gut feelings. It helps us identify more precise patterns and predictions than personal experience alone ever could.

Let’s return to grandma; she was convinced that relationships last longer if partners have mutual friends. This notion was based on her own experience, as she and her husband had spent many an evening with their friends in Queens, New York.

But in reality, her sample size was too small and the hard data suggests that she was mistaken. A 2014 study by Lars Backstrom and Jon Kleinberg, based on Facebook data, showed couples with more friends in common were more likely to change their relationship status from “in a relationship” to “single.”

This goes to show that though a gut feeling may get us far, data refine even the most intuitive person’s perspective.

Google is a good example of how big data can constantly provide new information.

Data science is a useful tool. But what makes it special is not the amount of collected data, but rather that the data is useful; in other words, it’s the kind of data that can reveal patterns or make predictions.

Google is a case in point. Larry Page and Sergey Brin’s search engine, founded in 1998, became such a giant not simply because they were able to collect lots of data. Rather, what set Google apart was that the collected data could be used efficiently.

Big data doesn’t lie.

Recently, graduates at the University of Maryland were surveyed about their grade point averages, or GPA. Among the respondents, two percent admitted they had graduated with a GPA lower than 2.5 on a four-point scale. However, according to official records, the number was much higher, at 11 percent.

While this is just one example, it demonstrates a universal truth about surveying: people lie.

Big data allows us to understand small data subsets, too.

It’s difficult to get your head around just how big big data is. Each day, staggering amounts of data fly through Google alone, not to mention other search engines or other websites in general. The volume of this data means we can now do things we could never do before.

This is the third great power of big data: the size of the datasets means we can zoom in on a subset and reliably extract information from it.

Big data makes A/B tests easier and cheaper to run.

Every day, we’re bombarded with stories about correlations. A food is linked with a disease. A habit is linked with success. These correlations seem credible at first, but correlation doesn’t necessarily imply a cause-and-effect relationship.

In reality, to learn about the causal effect of something, you’d need to establish causality by using randomized, controlled experiments, commonly called A/B tests. For instance, a study might report that people who drink alcohol only moderately are usually healthier. But does that imply that drinking moderately causes our health to improve? Of course not.

Big data isn’t great with too many variables or nonquantifiable concerns.

Although there are definite advantages to big data, it isn’t flawless. Its biggest limitation becomes patently clear in datasets with many variables: it’s difficult to extract reliable answers because the number of variables obscures possible findings.

Let’s consider the work of the behavioral geneticist Robert Plomin. In 1998, he thought he had found a gene, IGF2r, that was indicative of people’s IQ. He had obtained a dataset compiled from several hundred students containing information about DNA and IQ levels. Plomin compared their DNA with low and high IQs, and found that IGF2r was two times as likely to occur in students with high IQs.

Governments shouldn’t use big data to target individuals.

Every time you type in a Google search or shop for a product online, you’re contributing to big data – what about the ethical considerations? What if the government had access to this data? What could they do with it?

Say someone typed “I want to kill myself” into a search engine. Should the local police be notified?

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Worum geht es in Everybody Lies?

Everybody Lies (2017) is about the data collected in vast quantities by computers and over the internet. This data can help reveal fascinating information about the human psyche, behavior and quirks, because, as it turns out, people aren’t always so willing to communicate their true hopes and desires to others.

Wer Everybody Lies lesen sollte

Anyone interested in the complex nature of human behavior
Media studies experts and social scientists
Anyone concerned about the power of the internet and online privacy

Über den Autor

Seth Stephens-Davidowitz is an expert on internet data and big data in particular. He holds degrees from Stanford and Harvard Universities and worked previously as a data scientist at Google.

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