The World's Best Book Summaries
Can you train yourself to think in probabilities? How can this mental model help you make better decisions? In The Great Mental Models Volume 1, Shane Parrish and Rhiannon Beaubien explain how estimating probabilities can narrow down decisions. They discuss three types of probabilistic thinking: Bayesian thinking, fat-tailed curves, and asymmetries. Keep reading to learn how probabilistic thinking can help you navigate difficult decisions.
What is Charles Wheelan’s Naked Statistics about? What statistics does Wheelan cover in the book? Statistics help us use data to make sense of the world, and statistical insights help guide modern society, informing medical practices, public and fiscal policy, education initiatives, business and marketing decisions, and so on. But many people find statistics intimidating. In his book Naked Statistics, Charles Wheelan aims to demystify statistics to make them more accessible for non-mathematical audiences. Keep reading for our review of Naked Statistics, including the author’s background and commentary on the book’s approach.
What are the different types of bias in statistics? What are some ways bias can creep into a research project? As individuals and as a society, we rely on scientific research to make informed decisions and to understand the world around us. Therefore, researchers have an ethical obligation to identify and address sources of bias in their research. Statistical bias can make its way into a research project anywhere along the way, from the study’s conception to the research question, the data collection, the statistical analysis, the reporting of findings, and the study’s publication. Keep reading to learn about the
Do you take into account probability when making decisions? Why do we make decisions that contradict probabilistic logic? Understanding probability can be especially relevant to our daily lives because we make decisions based on our perception of probability all the time. However, our perception of likely outcomes is often mathematically irrational. This is because thinking in probabilities isn’t intuitive: Most people think in terms of binary categories of “yes,” “no,” and “maybe.” Here’s why we make decisions that contradict probabilistic logic.
What is inferential analysis? What can inferential statistics tell us about data? In simple terms, inferential statistics are a kind of combination of data and probability. Just as probability is never a guarantee of an outcome, there are no definitive answers in inferential statistics. Rather, inferential statistics help us use what we do know to make math-based best guesses about what we want to know. Keep reading for the ultimate guide to inferential statistics.
What is the correlation coefficient in statistics? What can the correlation coefficient tell us about the relationship between two variables? What is the danger in mistaking correlation for causation? A figure called the correlation coefficient quantifies the strength and direction of the relationship between two variables. A common mistake in statistics is equating correlation with causation. It can be tempting to extrapolate beyond a correlation coefficient, but that will lead to causal conclusions that correlation can’t support. In this article, we’ll break down the concept of statistical correlation and explain why correlation does not equal causation.
What is healthy-user bias? How can we isolate whether an intervention actually accounts for differences between individuals? Healthy-user bias occurs in studies that aim to assess the effect of a certain treatment or intervention. Because the people who choose to partake in such studies tend to be significantly different from their peers, it’s difficult to assess the degree to which the intervention (and not the participants’ characteristics) accounts for the findings. Keep reading to learn about healthy-user bias and how it affects research findings.
What’s the difference between correlation and causation? What are the consequences of mistaking correlation for causation? Just because two variables are correlated doesn’t mean one is causing the other. Correlation quantifies a relationship between two variables, but it doesn’t explain that relationship. This is a crucial distinction to keep in mind, as equating correlation and causation can lead to misinformed decisions. Here’s why correlation does not imply causation.
What is survivorship bias in statistics? How does survivorship bias skew our interpretation of the research findings? Any time a portion of a study sample is able to “leave” the study, we should be wary of survivorship bias. Survivorship bias occurs when we draw conclusions based on the “survivors” of a certain treatment or intervention. Keep reading to learn about survivorship bias and how it affects research findings.
What is the R2 statistic? What does the value of R2 tell us about the change in the dependent variable? The R2 statistic represents the proportion of the variance in the dependent variable that stems from the change in the independent variable. When using the R2 statistic to quantify the association between independent and dependent variables, it’s important to keep in mind that the R2 in linear regression only applies to linear relationships. It’s possible for two variables to be related, just not in a linear way. Here’s a look at the R2 statistic in linear regression.