For example, when we hear that more years of college education is positively correlated with a higher income, we conclude that additional years of university lead to greater wealth. We often immediately jump to - or are led to believe - A or B when C or D may be as likely. When two variables X and Y are correlated - meaning they increase together, decrease together, or one goes up as the other does down - there are four possible explanations:Ĭ.

Here are my lessons learned from “How to Lie with Statistics” with commentary from my experiences. The classic book discussed in this article addresses responsible consumption of data in a concise, effective, and enjoyable format. Fortunately, these core ideas don’t change much over time and often the best books on the subject (such as The Visual Display of Quantitative Information) are decades old. Compared to algorithms or big data processing, data literacy may not seem exciting, but it should form the basis for any data science education. These skills fall under a category called “data literacy”: the ability to read, understand, argue with, and make decisions from information. As consumers of information, we need to spot misleading/exaggerated statistics which manipulate us to take action that benefits someone else at our expense. As producers of tables and graphs, we need to effectively present valid summaries. This is why lessons from “How to Lie with Statistics” (by Darell Huff) are relevant even though each of us probably generates more data in a single day than existed in the entire world at the writing of the book.

Ultimately, that’s how the biggest decisions are made: with a few pieces of data a human can process. Time and again, I’ve seen thousands of work hours on complex algorithms summarized in a single number. How to Lie With Statistics is a 65-year-old book that can be read in an hour and will teach you more practical information you can use every day than any book on “big data” or “deep learning.” For all promised by machine learning and petabyte-scale data, the most effective techniques in data science are still small tables, graphs, or even a single number that summarize a situation and help us - or our bosses - make a decision informed by data.

How to lie using statistics