What is the central thesis of Everybody Lies?

The central thesis of "Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are" by Seth Stephens-Davidowitz is that the digital footprints we leave behind—especially through our online searches and social media interactions—provide a more accurate and insightful understanding of human behavior and societal trends than traditional surveys and self-reported data. The book argues that people often conceal their true feelings and intentions in social contexts, but their online data reveals their genuine thoughts and behaviors. By analyzing this data, we can uncover deeper truths about human nature, including topics related to sexuality, racism, and health, and challenge commonly held assumptions. Overall, the thesis emphasizes the power of data to reveal the complexity of human behavior in a way that traditional methods cannot.

How does the author define "big data"?

In "Everybody Lies," Seth Stephens-Davidowitz defines "big data" as large, complex data sets that can be analyzed to reveal patterns, trends, and associations, particularly relating to human behavior and preferences. He emphasizes the idea that traditional surveys and research methods often fail to capture the true nature of people's thoughts and actions because respondents may not be fully honest. Instead, big data—drawn from sources like internet searches, social media, and online interactions—provides a more genuine insight into human behavior, as these data reflect real activities and decisions rather than just stated preferences. By leveraging big data, the author argues that we can gain deeper insights into societal trends and individual motivations.

Why does the author argue that internet search data is more reliable than traditional survey data?

In "Everybody Lies," Seth Stephens-Davidowitz argues that internet search data is often more reliable than traditional survey data for several reasons:

1.   Anonymity and Honesty  : Internet searches provide a level of anonymity that surveys often do not. This anonymity allows individuals to express their true thoughts and desires without fear of judgment, leading to more honest data collection. People may be more willing to search for sensitive or taboo topics rather than admitting to them in a survey.

2.   Large Sample Size  : Search data comes from vast numbers of users, which can provide a more representative sample of the population. This abundance of data allows for more accurate insights into trends and behaviors, as opposed to the limitations of smaller survey samples.

3.   Real-Time Data  : Internet search queries reflect current interests, concerns, and behaviors in real-time. This immediacy means that search data can provide insights that are relevant to ongoing events or changing societal norms, while surveys may take time to conduct and analyze.

4.   Behavioral Insights  : Search data captures actual behavior rather than reported behavior. People might claim to behave a certain way in surveys but search data reveals what they truly desire or worry about, providing a clearer picture of human behavior.

5.   Elimination of Biases  : Traditional surveys can suffer from various biases, including sampling bias or response bias. Search data is more likely to reflect a natural distribution of thoughts and inquiries without being subjected to the same pitfalls.

By examining this data, Stephens-Davidowitz believes researchers can uncover truths about society and human behavior that might otherwise remain hidden in self-reported data.