The World's Best Book Summaries
What is the doppelganger effect in data? How is the method used to study people? A big data technique Seth Stephens-Davidowitz identifies is the doppelganger method. It’s a technique where researchers make predictions about one person by studying another person who’s statistically similar to the first person. Learn more about the power of doppelgangers, as explained in Everybody Lies.
What is the main function of the hormone insulin? What happens when your insulin stops functioning properly? Insulin is a hormone, a molecule that transmits “messages” within your body. Its function is to deliver glucose to cells to use for energy and take the excess glucose to the liver. Learn about the role of insulin in the body and what happens when it stops working as intended.
What is the central limit theorem in statistics? What can the central limit theorem tell us about the distribution of the sample mean? The central limit theorem states that the mean of a representative sample will be close to the mean of the larger population. Therefore, we can confidently make inferences about a population from a sample or about a sample from a population, and we can compare samples to each other. Let’s explore each of these general applications of inferential statistics with an example.
What are the most memorable quotes from Everybody Lies? How can these quotes help you understand data science? Everybody Lies, by Seth Stephens-Davidowitz, is about big data’s potential to revolutionize social science research. The book’s central premise is that people reveal more about themselves when making web searches than they would ever reveal in public or in a traditional survey. Read more for a few Everybody Lies quotes to explain Stephens-Davidowitz’s argument.
What is sampling in research methodology? Why is having a representative sample important? Many research and survey projects rely on sampling as a way to learn about a larger population. Researchers aim to select samples that reflect the target population as closely as possible. Keep reading for the definition of a representative sample, why it’s important, and how to collect samples that are representative of the population in question.
What is the null hypothesis in statistics? What does accepting a null hypothesis tell us about the relationship between two variables? When we use inferential statistics to answer a question, we begin with a null hypothesis. A null hypothesis assumes a relationship between two variables that we’ll accept or reject. If the null hypothesis is rejected, we accept the alternative hypothesis, which is the logical inverse of the null hypothesis. Keep reading to learn about the null hypothesis, why we use it, and what accepting/rejecting it can tell us about data.
What is the book Everybody Lies about? What should you take away from the book? In Everybody Lies, Seth Stephens-Davidowitz argues that people willingly confess all of their secrets in their Google searches and other web activity. This information can be found through big data and can be used for the greater good. Read below for a brief overview of the book Everybody Lies by Seth Stephens-Davidowitz.
What are Type I and Type II errors in hypothesis testing? How can you minimize your chances of accepting a wrong hypothesis? Type I and Type II errors both relate to the result of the null hypothesis. A Type I error occurs when the null hypothesis is mistakenly rejected, whereas a Type II error occurs when the null hypothesis is mistakenly accepted. Keep reading to learn about the difference between a Type I and a Type II error, and how to reduce your chances of making both.
What is big data? Why does Seth Stephens-Davidowitz care about it? In Everybody Lies, Seth Stephens-Davidowitz says information from big data can be used for the greater good. But to do so, data researchers have to understand big data’s inherent strengths—and avoid its inherent weaknesses. Learn more about big data, Seth Stephens-Davidowitz’s definition of it, and why it’s important for research.
What are the benefits of big data? Do you want to know how to use data well? Despite all of the potential advantages, Seth Stephens-Davidowitz acknowledges that it’s easy to use big data ineffectively. To get the most out of big data, Stephens-Davidowitz says you should focus on its four main benefits: new types of information, unprecedented honesty, high resolution, and easy cause-effect analysis. Keep reading for the four benefits of big data that are explained in Everybody Lies.