1-Sentence-Summary: The Art of Statistics is a non-technical book that shows how statistics is helping humans everywhere get a new hold of data, interpret numbers, fact-check information, and reveal valuable insights, all while keeping the world as we know it afloat.
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Favorite quote from the author:
In today’s world, statistical science is everywhere. It helps us to understand how humans work, how the market works, and how our environment is changing.
The Art of Statistics is an introduction to the basic concepts of statistical science written in a less technical way. The book explains how statistics are humans everywhere answer questions, discover relevant insights, and add more facts to their stories.
The book introduces readers to the basic tools of data analysis: graphs, charts, tables, and maps. It also explores how we can use data to answer questions based on samples.
The author covers some common problems that arise when trying to collect good data—such as selection bias and measurement error—and describes some strategies for dealing with them effectively.
Here are three of my favorite lessons from the book:
- Statistics offer accurate results, but it’s the humans who misinterpret the data right from the beginning.
- Always check your sources and filter out the media factor when interpreting data, as they tend to inflate it.
- People assume that because two things are correlated, they caused a certain outcome, but that’s a fallacy.
Statistics are not dull at all, and this is what you’re going to learn in the following paragraphs. Let’s take each lesson and study it carefully below!
Lesson 1: Statistics can only offer so much information when humans are involved in the collection of data.
Data is only one part of the story. Statisticians use numbers and patterns to create their studies, but part of their job is to understand what they’re measuring first. If their data comes from a study where people were surveyed, the final product may not be quite an objective truth.
Instead, the result is a representation of the study. Consider that a lot of data is collected from focus groups or questionnaires that ask people to relate to their experiences.
Therefore, data may be biased (e.g., if you’re interviewing women about their experiences with sexual harassment and you ask men, your data will be very different). However, statistics are still highly useful in our world.
They help us in all areas of work and make a huge difference in time and money spent on projects. They also offer faster and better results (if the data input is correct) and help provide answers in critical situations.
For example, if you’re chasing a murderer as a policeman, you’re likely going to use statistics to calculate common patterns. This is how plenty of detectives came to a breakthrough discovery in their cases.
Lesson 2: The media often inflates data so as to increase their click-through rate.
Data is often displayed in a way that is meant to be eye-catching and exciting but could be misleading. For example, consider the recent study on the effects of coffee consumption on heart disease. The results were presented as follows:
“Drinking coffee every day can reduce your risk of heart disease”. This sounds like good news for coffee drinkers, but it’s not quite that simple. The researchers found that drinking two to three cups of coffee per day reduced your risk of heart disease.
However, there’s something important missing here: what if you only drink one cup per day? Or four cups? What about six? It turns out that this study doesn’t tell us much about how many cups are safe to drink; it only tells us how many cups are beneficial.
The media tends to take this type of information and present it in a way that makes it seem more definitive than it really is—and often exaggerates the findings to get people clicking through their sites and sharing articles on Facebook.
You should be able to interpret the data you’re looking at because the media often over-exaggerates it in order to increase web traffic. The way data is displayed has a large impact on how we use it, so check your sources carefully.
Lesson 3: Just because two things are correlated, it doesn’t mean that they caused a certain outcome.
Correlation is often seen as caused by people, but that’s simply a misconception most of the time. Unfortunately, the media and those who love good gossip like to twist such manipulable information to an explosive outcome.
For example, when we hear that a new study has found that people who drink coffee are more likely to be more productive at work, we might naturally conclude that drinking coffee makes people more productive.
Of course, this isn’t necessarily true: perhaps those who drink coffee are simply more likely to have chosen careers where productivity is valued and rewarded, or that’s where the study was conducted.
The same thing can happen with health statistics. Numbers don’t lie, but people can. For example, one study found that women who ate meat were less likely to get cancer than women who didn’t eat meat—but this doesn’t mean that eating meat will prevent cancer.
It could just mean that women who don’t eat meat are also less likely to get cancer for other reasons (maybe they exercise more or smoke less). People and certain studies fail to include essential factors in their research a lot of times, which leads to misinterpretations.
The Art of Statistics Review
If you’re like me, you might be a little intimidated by the idea of statistics. I mean, who wants to spend their free time studying data sets when they could be watching Netflix? Well, The Art of Statistics might change your perception!
The book is packed with fun examples that illustrate how statistics are used every day—and it even explains how to do the math yourself if you want to get into the nitty-gritty of it all. I recommend it to anyone who loves a good read on weekends.
Who would I recommend The Art of Statistics summary to?
The 23-year-old statistics student who is passionate about this subject, the 34-year-old marketing director who wants to learn more about data’s impact on the world, or the 29-year-old concerned citizen who wants to learn how to filter out fake data from relevant studies.
Last Updated on December 30, 2022
