Everybody Lies: Summary Review
This is a summary review of Everybody Lies containing key details about the book.
What is Everybody Lies About?
"Everybody Lies" is a book by data scientist Seth Stephens-Davidowitz. It is an insightful and engaging book that provides a unique perspective on the power of data to uncover the truth about human behavior. This book a good-read for anyone interested in technology, data science, and human behavior.
Insightful, funny and always surprising, Everybody Lies explores how this huge collection of data, unprecedented in human history, could just be the most important ever collected. It offers astonishing insights into the human psyche, revealing the biases deeply embedded within us, the questions we're afraid to ask that might be essential to our well-being, and the information we can use to change our culture for the better.
Summary Points & Takeaways from Everybody Lies
Some key summary points and takeaways from the book include:
* The book explores how large amounts of data from the internet can be used to gain insights into human behavior and attitudes.
* Stephens-Davidowitz argues that people are more likely to tell the truth about sensitive or taboo topics online than they are in other forms of communication, such as surveys or face-to-face interviews.
* The author uses data from sources such as Google search queries and online forums to uncover a wide range of interesting insights into human behavior, including how people really feel about topics like race, sex, politics, and religion.
* He also shows how this data can be used to make predictions and identify patterns, including trends in public opinion, shifts in consumer behavior, and changes in social attitudes.
* The book highlights the limitations of traditional forms of data collection, such as surveys and polls, and explains why online data is a more accurate and comprehensive way of understanding human behavior.
* Stephens-Davidowitz also discusses the ethical implications of using this kind of data, and stresses the importance of using it responsibly and for the greater good.
Who is the author of Everybody Lies?
Seth Isaac Stephens-Davidowitz is an American data scientist, economist, and author. He is a New York Times op-ed contributor and a former data scientist at Google, as well as a former visiting lecturer at the Wharton School of the University of Pennsylvania.
Everybody Lies Summary Notes
Summary Note: Data Science is More Intuitive Than You Think
Data science is often associated with big data, which refers to vast amounts of data that require computational power to recognize patterns. However, despite its remarkable scale, data science has an intuitive aspect to it. In fact, we all engage in data science in our daily lives, as demonstrated by the author's grandmother who used years of information and data gathering to articulate characteristics she saw as essential in successful relationships. She was utilizing information to spot patterns and make predictions, just like a data scientist.
However, while intuition plays a role in data science, it is not enough on its own. Data provides us with the material to confirm or challenge our initial gut feelings. It helps us identify more precise patterns and predictions than personal experience alone ever could. The author gives an example of how his grandmother believed that relationships last longer if partners have mutual friends, based on her own experience. However, a study based on Facebook data showed that this belief was mistaken, indicating that data can refine even the most intuitive person's perspective.
This highlights the importance of utilizing gathered data correctly in refining our worldview. While intuition can be a valuable tool, it is not infallible, and data can provide more accurate insights. Data science involves using data to make informed decisions and predictions, and it requires critical thinking, analysis, and interpretation of data. It is a blend of intuition and evidence-based reasoning, and it can help us uncover hidden patterns and insights that may not be immediately apparent.
In today's data-driven world, understanding the intuitive aspect of data science and the importance of using data to refine our perspectives is crucial. It allows us to make more informed decisions, challenge our assumptions, and gain a deeper understanding of complex issues. So, whether it's in our personal relationships or in professional settings, embracing data science as a blend of intuition and evidence-based reasoning can lead to better outcomes and insights.
Summary Note: Google's Example of Big Data's Constant Information Flow
Big data is not just about the volume of data collected, but rather the usefulness of the data in revealing patterns and making predictions. Google's success as a search engine is a prime example of how big data can be used efficiently to provide constant streams of new information. Before Google, search engines would simply provide websites containing the searched phrase most frequently, resulting in irrelevant hits. However, Google's algorithm, developed by Larry Page and Sergey Brin, took into account the number of links from other sites pointing to a website, indicating its relevance. By aggregating data about links, Google was able to spot patterns and predict the most relevant information for users.
One of the reasons why big data is so powerful is because it constantly provides new information. In the past, waiting for official reports or surveys from organizations like the Bureau of Labor Statistics or the Centers for Disease Control and Prevention was necessary to obtain current data on topics such as unemployment rates or disease spread. However, with big data, information can be obtained in real-time. For example, Google engineer Jeremy Ginsberg used flu-related Google searches to track the spread of influenza across geographical areas and over time. By analyzing search queries such as "flu symptoms," Ginsberg was able to identify patterns and make predictions about the spread of the disease.
Google's approach to utilizing big data demonstrates the importance of not just collecting data, but also effectively using it to uncover insights and make informed decisions. The constant flow of new information provided by big data allows for real-time tracking of various phenomena and can lead to more accurate predictions and better decision-making. This example highlights the power of big data in transforming industries and enabling organizations to gain valuable insights from vast amounts of information. It also underscores the need for data scientists and analysts to harness the potential of big data through efficient data collection, analysis, and interpretation.
Summary Note: Big Data Doesn't Lie: Unveiling Truths through Online Behavior
Big data is a powerful tool that provides insights into human behavior and reveals truths that may not be easily obtained through surveys or interviews. Surveys are often subject to social desirability bias, where respondents may provide answers that make themselves look better or impress the person administering the survey. However, big data collected through unfiltered online behavior is not influenced by such biases and provides a more accurate picture of reality.
The example of surveying GPA among graduates at the University of Maryland demonstrates how people may lie or skew results to present a more positive view of themselves. In contrast, big data collected from online behavior, such as search engine queries or website visits, reflects genuine interests and behaviors without the influence of social desirability bias. This makes big data a more reliable source of information for understanding human behavior, thoughts, desires, and beliefs.
Another advantage of big data is its ability to reveal unexpected truths that people may not be willing to share directly with others. For example, the analysis of data from a porn website showed that some women were searching for "anal apple," indicating an interest in anal play. This highlights how big data can uncover surprising and sometimes taboo aspects of human behavior that may not be revealed through traditional surveys or interviews.
Summary Note: Big data allows us to understand small data subsets, too.
The power of big data lies in its ability to provide insights at various scales, from the macro to the micro level. With the vast amount of data available, we can zoom in on smaller subsets of data and extract valuable information from them. This is exemplified by the work of Harvard professor Raj Chetty, who used big data to investigate the American dream.
Chetty and his team utilized tax records collected by the US Internal Revenue Service, comprising over one billion tax observations. They initially found that the chances of a poor American achieving success in their chosen field were lower compared to other developed countries like Denmark and Canada. However, the beauty of big data was revealed when Chetty zoomed in on different states, cities, towns, and neighborhoods.
Through this zooming-in approach, Chetty discovered that the American dream existed but was only attainable in a few specific places. For example, a poor American growing up in San Jose, California, had a 12.9 percent chance of getting rich, better than in Denmark. However, for those in Charlotte, North Carolina, the chances were only 4.4 percent. This ability to analyze data at a granular level allowed Chetty to gain a nuanced understanding of the world and uncover insights that would have been impossible with smaller datasets.
The power of big data lies in its ability to provide detailed insights into small data subsets, revealing patterns and trends that may not be apparent at a larger scale. It allows researchers, policymakers, and businesses to make more informed decisions and understand the complexities of human behavior and societal dynamics. Big data has revolutionized our understanding of the world, providing us with unprecedented opportunities to gain insights from data and make meaningful changes based on evidence.
Summary Note: Big Data Makes A/B Tests Easier and Cheaper to Run
One of the main themes highlighted in the book is how big data has made A/B testing easier and cheaper to run. A/B testing, also known as randomized controlled experiments, is a crucial method to establish causality and determine the impact of a specific variable on an outcome. Traditionally, running A/B tests required significant resources, including recruiting participants, conducting surveys, and analyzing results. However, big data has revolutionized this process by enabling data scientists to write programs that can automatically analyze large datasets from A/B tests.
The book uses an example from Barack Obama's 2008 presidential campaign to illustrate the power of big data in A/B testing. Campaign directors used different combinations of pictures and text on their website to entice people to sign up and donate. By analyzing the data collected from these different variations, they were able to deduce which layout was the most successful.
The book also highlights the importance of establishing causality in data analysis. Correlations between variables may seem credible at first, but they do not necessarily imply a cause-and-effect relationship. A/B testing is a critical tool to determine causality and understand the true impact of a variable on an outcome.
Summary Note: Big Data Limitations with Multiple Variables and Non-Quantifiable Concerns
One of the main themes discussed in this book is the limitations of big data when dealing with datasets that have many variables or non-quantifiable concerns. While big data has definite advantages, it is not flawless and can face challenges when it comes to extracting reliable answers in complex datasets.
The book uses an example from the work of behavioral geneticist Robert Plomin to illustrate this point. Plomin initially found a correlation between a gene called IGF2r and high IQ levels in a dataset compiled from several hundred students. However, when he repeated the dataset comparison a few years later, the correlation was nowhere to be seen. This highlights how the sheer number of variables in big data can obscure possible findings, and how correlations can occur by chance.
Another limitation of big data discussed in The book is the lack of small data, which is about the human experience. While big data can measure a lot, it may not necessarily provide insights into people's experiences, opinions, and non-measurable aspects. For example, while Facebook can easily measure clicks and likes using big data, it may not capture users' true experiences with the site.
The book emphasizes that in such circumstances, small data becomes essential. Companies like Facebook gather small data through smaller-scale surveys and by employing psychologists and sociologists to gain insights into non-measurable user experiences. This highlights that big data is not perfect and has limitations when it comes to understanding complex human experiences and non-quantifiable concerns.
Summary Note: Governments Shouldn't Use Big Data to Target Individuals.
The ethical considerations of governments using big data to target individuals is a main idea explored in the book "Everybody Lies". While big data offers valuable insights and correlations, it also raises questions about privacy, ethics, and the appropriate use of data by governments. The sheer volume of data generated by individuals through online searches and online activities, such as online shopping, presents opportunities for governments to potentially monitor and intervene in people's lives. However, the ethical implications of such actions need to be carefully considered.
One example discussed in the book is the issue of suicide-related searches. While identifying individuals who may be at risk of suicide based on their online searches could potentially help prevent suicides, it also raises concerns about invasion of privacy and the appropriate use of data. With millions of suicide-related searches happening each month, it would be impractical for authorities to respond to each search individually. Furthermore, the correlation between online searches and actual suicide rates may not be accurate at an individual level, but rather at a regional or state level.
The authors highlight the need to carefully consider the ethical implications of governments possessing and using individual-level search data. While big data can provide valuable insights and be used for productive purposes, it also poses risks in terms of invasion of privacy and potential abuse of power. Instead of targeting individuals, governments could use big data in more responsible and localized ways, such as in state or municipal-level suicide prevention programs. This could involve disseminating information through media channels with tips on where to seek help, without compromising individuals' privacy.
The key takeaway from this idea is that while big data offers immense potential for understanding human behavior and addressing societal issues, its use by governments should be guided by ethical considerations. The appropriate balance between utilizing big data for the greater good and respecting individuals' privacy and rights must be carefully navigated. Governments should be cautious about using big data to target individuals and should focus on responsible and localized use of data for the benefit of society as a whole.
Book details
- Print length: 338 pages
- Genre: Nonfiction, Science, Psychology
What are the chapters in Everybody Lies?
Chapter 1. Your Faulty Gut
Chapter 2. Was Freud Right?
Chapter 3. Data Reimagined
Chapter 4. Digital Truth Serum
Chapter 5. Zooming In
Chapter 6. All the World’s a Lab
Chapter 7. Big Data, Big Schmata? What It Cannot Do
Chapter 8. Mo Data, Mo Problems? What We Shouldn’t Do
What is a good quote from Everybody Lies
Top Quote: “The next Freud will be a data scientist. The next Marx will be a data scientist. The next Salk might very well be a data scientist.” (Meaning) - Everybody Lies Quotes, Seth Stephens-Davidowitz
What do critics say?
Here's what one of the prominent reviewers had to say about the book: "Stephens-Davidowitz, a former data scientist at Google, has spent the last four years poring over Internet search data . . . What he found is that Internet search data might be the Holy Grail when it comes to understanding the true nature of humanity." - New York Post
* The editor of this summary review made every effort to maintain information accuracy, including any published quotes, chapters, or takeaways. If you're interested in furthering your personal development, I invite you to check out my list of favorite personal development books page. On this page, you'll find a curated list of books that have personally impacted my life, each with a summary and key lessons.
Chief Editor
Tal Gur is an author, founder, and impact-driven entrepreneur at heart. After trading his daily grind for a life of his own daring design, he spent a decade pursuing 100 major life goals around the globe. His journey and most recent book, The Art of Fully Living, has led him to found Elevate Society.
