Everybody Lies audiobook cover - Big Data, New Data and What the Internet Can Tell Us About Who We Really Are

Everybody Lies

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

Seth Stephens-Davidowitz

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Categories:
EconomicsPsychology

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Key Takeaways from Everybody Lies

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Everybody Lies
The Core Premise+
Nature of Data Science+
Four Powers of Big Data+
Limitations+
Ethics & Application+

Quiz — Test Your Understanding

Question 1 of 7
How does the author describe the relationship between human intuition and data science?
  • A. Data science completely replaces human intuition because gut feelings are inherently biased.
  • B. Data science is an intuitive process where data is used to refine and test our initial gut feelings.
  • C. Human intuition is generally more reliable than data science when the sample size is small.
  • D. Data science proves that our intuitive predictions about human behavior are almost always correct.
Question 2 of 7
According to the text, what made Google's early search engine algorithm uniquely successful compared to its predecessors?
  • A. It collected a significantly larger volume of data than any other search engine at the time.
  • B. It relied on user surveys to verify the accuracy and helpfulness of the websites it indexed.
  • C. It determined a website's relevance based on the number of links from other sites pointing to it.
  • D. It tracked users' geographical locations to provide hyper-local search results.
Question 3 of 7
Why does the author argue that traditional surveys are often unreliable for understanding human behavior?
  • A. Survey sample sizes are usually too small to draw statistically significant conclusions.
  • B. People frequently exhibit 'social desirability bias,' adapting their answers to look better to themselves and others.
  • C. Survey questions are typically formulated with too many variables, confusing the respondents.
  • D. Most surveys are conducted completely anonymously, which encourages respondents to give random answers.
Question 4 of 7
How did Harvard professor Raj Chetty utilize big data to investigate the state of the 'American dream'?
  • A. He analyzed millions of Google searches related to job hunting and career advancement.
  • B. He ran large-scale A/B tests on social media to see how people in different states responded to economic opportunities.
  • C. He tracked the online spending habits of low-income families across different developed countries.
  • D. He used over a billion tax records to show that a poor person's chances of getting rich vary drastically depending on their specific city.
Question 5 of 7
According to the book, what is the most effective way to establish a cause-and-effect relationship rather than just a correlation?
  • A. Gathering a massive volume of observational data over a long period of time.
  • B. Zooming in on small subsets of big data to eliminate outliers.
  • C. Conducting randomized, controlled experiments, commonly known as A/B tests.
  • D. Cross-referencing search engine data with official government statistics.
Question 6 of 7
What is identified as a major limitation of big data when dealing with datasets that have many variables?
  • A. It becomes too expensive and computationally demanding to process the information.
  • B. It can lead to fluke correlations that happen purely by chance, as seen in early genetic studies on IQ.
  • C. It inevitably triggers social desirability bias, making the resulting data untrustworthy.
  • D. It prevents data scientists from being able to zoom in on specific local subsets.
Question 7 of 7
Why does the author argue against police using big data to track down individuals who search for phrases like 'I want to kill myself'?
  • A. The number of suicide-related searches vastly outnumbers actual suicides, which would result in a massive waste of police resources.
  • B. Search engines are legally prohibited from sharing individual search histories with law enforcement agencies.
  • C. Individuals who make these searches almost always use anonymous browsers, making them impossible to track.
  • D. Big data algorithms cannot accurately distinguish between searches made for academic research and those made by individuals in distress.

Everybody Lies — Full Chapter Overview

Everybody Lies Summary & Overview

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.

Who Should Listen to Everybody Lies?

  • 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

About the Author: Seth Stephens-Davidowitz

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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