The World's Best Book Summaries
What is the difference between mean and mode? Which of the two is a more accurate measure? Both the mean and the mode are measures of central tendency. For non-skewed distributions, the mean is more accurate because it takes into account every value in the data set. For skewed data, the median is better because it isn’t influenced by outliers. Keep reading to learn about the difference between mean vs. mode and when to use which.
What are some interesting facts from Everybody Lies? What do these facts tell us about big data? Everybody Lies draws on data scientist Seth Stephens-Davidowitz’s research using Google search results as well as data from PornHub, Wikipedia, and more. The book contains many surprising and fascinating findings from his research. Read more for Everybody Lies‘s big data facts that will surprise you.
What is Charles Wheelan’s Naked Statistics about? What statistics does Wheelan explore in the book? Naked Statistics puts the math behind statistics into digestible terms and explains statistics concepts with relatable, relevant, and even humorous examples. Readers also benefit from additional socio-political insight from the book, as Wheelan uses real-world anecdotes to explore how statistics can inform collective decision-making. Below is a brief overview of the key themes and concepts from Wheelan’s Naked Statistics.
What is significance in statistics? What does “statistically significant” mean? In statistics, significance refers to the degree of confidence or certainty in the results of a statistical analysis being attributable to a specific cause. When results are said to be “statistically significant,” the observed relationship between variables is unlikely to be due to chance. Keep reading to learn about the concept of statistical significance, explained in simple terms.
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.
Why is reliability important in data collection? What are the main challenges inherent in collecting reliable data? As we use statistical data to inform our lives and society, we need them to be both accurate and precise. Therefore, collecting quality data is the true challenge and art of producing reliable, constructive statistics. Keep reading to learn about the importance of reliability in data collection.
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 hypothesis testing? How do you know if a hypothesis is true? Hypothesis testing is an inferential statistical method by which we determine whether our tentative assumptions are true. Based on our statistical analyses, we can either accept these hypotheses as true or reject them as false with varying degrees of certainty. Let’s look at the common conventions around inferential statistics and hypothesis testing.
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.