Everydata Book Review

Everydata Book Review

John Johnson and Mike Gluck’s Everydata: The Misinformation Hidden in the Little Data You Consume Every Day (“Everydata”) informs and educates readers on how to properly interpret, analyze, scrutinize, and process data. In my opinion, the book covers best practices about data that every person should learn, understand, and apply in his everyday life to make more informed and better decisions to further his own self-interests. As a professional working in finance, it is also better for business if investors are acting rationally. A similar idea applies to society. The book is not for everyone. You need to be open to the fact that you have shortcomings in regards to how you consume data and want to learn how to improve. As the saying goes, you cannot fill a cup that is already full. At one point in the book, the authors outwardly state that they do not intend it to be a text book. Nevertheless, it is at least text book adjacent and is best utilized as educational material. From my perspective, it is perfect as a supplemental book to a beginning to intermediate business class on statistics. There are even a couple of pages at the end of each chapter that summarize the key takeaways, which is an excellent attribute in a good text book. While the book is well written and utilizes real life and brilliant examples/ stories to explain concepts in a way that anyone can understand, the book is not written to entertain with the stories. The examples and stories are mainly used to explain the key points of the book. For example, Freakonomics (2005) and SuperFreakonomics (2009) analyzes various situations and looks for unique and provocative conclusions that captivate the reader. Accordingly, those books are a great read because their outlandish suggestions are deliciously entertaining even if you ultimately do not agree with them. Everydata is not the same style of book. If you are not interested in being educated about data, it would be a waste of time for you because you will not appreciate the book and probably stop reading it. If you do decide to read and finish the book, it will provide an eye opening experience. I have already read a book back in 2009, John R. Nofsinger’s The Psychology of Investing, that covers similar topics. It completely changed how I viewed data and the world. I believe the lessons in the book help me make better decisions today. Again, I recommend everyone learn the same teachings. If you are interested in them, Everydata is an excellent book to read. However, I am realistic. I understand that not everyone is receptive to the ideas.

So who and who should not read Everydata? I will borrow some great quotes the book cites from various sources and studies. The Boston Globe wrote “Facts don’t necessarily have the power to change our minds”. A University of Michigan study found “people who were misinformed often held fast to their beliefs; some even felt more strongly in their (false) beliefs when faced with facts”. Next, “55 percent of Americans think they are smarter than average” and “93 percent said they were more skillful than the average (median) driver”. Of course, it is statistically impossible for all of the 55 percent and 93 percent to be correct. In addition, human beings are compelled by and want to learn from individual stories because they are memorable. However, it is important to realize that a story is just one data point and cannot be used to paint a complete picture or draw accurate conclusions by itself. Everydata beautifully quotes “The plural of anecdote is not data”. If you are an individual who unequivocally relies entirely on your gut feeling, that practice has reasonably or greatly worked out for you, and you think the above statements are complete nonsense because you believe you are above them; you are not the target audience. Personally, I believe in playing the odds as much as possible. Of course, doing so only means you are going in the direction that is more likely to succeed (over 51% and hopefully much greater). It does not mean it will definitely happen. The world is impossibly complex and there are too many “X” factors that can affect an outcome. As a result, individuals can still overcome the odds (e.g. Lebron James and the Cleveland Cavaliers overcoming a 3 games to 1 deficit to beat a record setting 73 win Golden State Warriors team in the NBA Finals). For someone who consistently goes against the odds and wins, he could just be that talented and gifted, like a Lebron James, to overcome them or he could have just got lucky. Another possibility is that he only remembers and boasts about his successes and conveniently forgets his failures. I prefer to make decisions that give me the best chance at success so I can best utilize my best abilities. Nevertheless, I do see a benefit in balancing understanding and playing the odds with gut feelings or great instincts. If the probabilities are 51%, 60%, or 70%; there is enough probability when something will not happen that going with gut feeling is not crazy. On the other hand, there is no harm in having a better understanding of a situation even if you eventually go with your heart. If the probability of failure starts to be greater than over 80 or 90%, it may be a good idea to reconsider and override your gut with your head. In the end, we are all responsible for our own fates so we will do what is comfortable for us. I highly recommend reading and learning from a book like Everydata but I make no demands. It is a conscious choice each individual needs to make on his own.

If you do decide to read Everydata, the authors provide a thorough and well written book that provides a lot of great real world examples and stories that illustrate the concepts. Even if you do not have much knowledge in statistics and data, they do a good job starting with basics and working up. On the other hand, getting into the book can be a struggle if you already have already taken a statistics class. I had a difficult time reading through the explanations of simple statistical concepts like mean, median, and mode. In those parts of the book, I skimmed through a good amount of it. Nevertheless, I did appreciate that those sections were well written with great examples. An excellent example of how the authors utilize real life stories to explain their points is sampling. One of the most vivid stories they tell is the tragedy of the space shuttle Challenger’s explosion during takeoff in 1986. After an investigation, it was determined that the small O-rings in the shuttle caused the explosion. Everydata notes that the issue had a lot to do with poor sample selection. When testing the O-rings before the doomed flight, NASA only tested the rings in hot weather and determined what conditions would lead to a problem in extreme hot conditions. Unfortunately, they failed to properly create a sample that would account for all conditions. They erroneously did not test the rings in cold weather, which were the conditions present during the tragedy. Similarly, Everydata explains how sampling is used for the purposes of the U.S. Census to count the population in each state which determines the Electoral votes for the respective states that decides Presidential Elections. In addition, the book goes over how the gluten free market, 44 million people or 29 percent of the total population of the U.S., drastically exceeds the amount of people who suffer from celiac disease and need to avoid gluten, 2.4 million or one percent. Naturally, you would get a different answer on the prediction of the market size if you sample the entire population of the country, where a lot of individuals voluntarily give up gluten for dietary purposes, or just the Celiac Awareness Group who would answer with the number of people who are required to exclude gluten from their diet.

Another fantastic instance when Everydata utilizes examples and stories to explain an idea is correlation vs. causation. Correlation means there is a relationship between two variables whether they are actually linked or not. Causation means that one variable actually affects and causes the other. A brilliant example the book uses to explain it is with Starbucks shops. It notes that houses that are closest to a Starbucks appreciate more than 20 percent during a five year period after the shop opens. One may conclude that the Starbucks shop actually causes the house prices to increase. In reality, it is actually a correlation rather than causation. Starbucks, as well as any other well-managed chain, performs market research before opening a store. In this situation, they put a store in the area because they anticipated the area growing which causes housing prices to increase and provides a viable market to sell coffee. Accordingly, the appreciation in house prices caused the Starbucks to be built and not the other way around. The book also does a great job explaining how human beings have a need to “fill in the blanks” and create causation even when there is none. It uses a common occurrence in society that anyone can understand: sports fans. When you watch or attend a sporting event, you will see fans fashion lucky jerseys or other good luck charms in a futile attempt to affect the game. Of course, the athletes on the field determine the outcomes of the games. Nevertheless, it does not stop sports fans from implementing their superstitions. Even though I know it is complete nonsense, I cannot stop myself from doing it myself. Similarly, every person has a confirmation bias. We have a “tendency to interpret data in a way that reinforces your preconceptions”. Even though my mind tells me good luck charms and superstitious routines do not affect sporting events, my heart looks for the first signs they are working. As we are in the midst of a heated Presidential race, you will see plenty of confirmation bias from die hard party loyalists. As one could conclude, Everydata does an excellent job integrating real life and relatable stories and situations that add flavor to material that would otherwise be dry. More importantly, it does it in a way that makes the concepts easier to grasp.

When I read The Psychology of Investing, it significantly changed and improved how I view the world and make decisions [hopefully for the better]. Similarly, Everydata provides a lot of great takeaways. One of the most important lessons it teaches is the difference between misrepresented vs misinterpreted. Misrepresentation is the source of the information purposely manipulating data in an attempt to trick you into forming a false conclusion. Misinterpretation is the user not processing the information correctly. Both can be pitfalls when dealing with data. Another critical takeaway is the use of cherry picking data. Everydata goes through a misleading claim Gerber used to sell its baby food. Gerber boasted that “four out of five pediatricians who recommended baby food recommend Gerber”. Technically, the statement is true. Gerber started its survey with 562 pediatricians. 408 responded that they recommended baby food. 76 recommended specific brand and 67 of those pediatricians recommended Gerber. Accordingly, they cherry picked the 76 who recommended a specific brand. If they stated that 76 of 562 (12%) pediatricians recommended Gerber, it would not sound impressive at all. Consequently, the statement is true but misleading. Identically, you have already seen and will see plenty of cherry picking from both Presidential campaigns. In my opinion, the most important takeaway from Everydata, The Psychology of Investing, and other similar books is the concept of projection vs. prediction: what should happen compared to what will actually happen. Everydata uses the same brilliant example I see from other books and sources, the coin flip. The odds are simple to understand. It is 50/50 that a coin will land on heads or tails when flipped. However, the probabilities get slightly more complex as you make more flips. When you flip it ten times, the chances of landing on heads and tails 5 times each is only 25%. When you flip it one hundred times, the odds drop to 8%. Nevertheless, the odds of 50/50 does not change on each individual flip. Think about that point for a second. The thought that your odds improves on landing on heads if there are consecutive tails is a “gambler’s fallacy”. Casinos design their games with the odds in their favor. Over time and after a large sample size, the house always win. They are banking on foolish gamblers believing in the gambler’s fallacy. A wise gambler will bring only enough money they are willing and can afford to lose because the odds are never in his favor even if he wins big sometimes. Of course, a gambler’s fallacy is a part of the psychology of how the brain works. Other critical psychological factors are overconfidence, fear, and idiosyncratic rater effect [As Everydata quotes the Harvard Business Review: “61% of my rating of you is a reflection of me”]. Again, I truly believe these key takeaways are principles each person should understand. Everydata does an excellent job explaining all of them.

Everydata is a very well written book covering very important principles related to data. If the concepts presented in the book interest you and you want to learn more, Peter L. Bernstein’s Against the Gods is another excellent book I recommend (/2015/11/09/against-the-gods-book-review/). It covers similar ideas but is centered on risk management. One shortcoming of Everydata is that it mainly focuses on the problems with data consumption but not the solutions that will lead to better decision making. For this purpose, I also recommend reading the aforementioned The Psychology of Investing which will provide great insight on how to apply the principles to make better investment choices.

Pat Wong

About Pat Wong

Patrick is a contributor for Rookerville. He is an avid sports fan. Before joining Rookerville, he was part of a defunct New York Yankees message board, NYYankeefans, where he was its top poster and was inducted in its Hall of Fame for his contributions. Patrick is also a passionate fan of movies. He has enjoyed reading movie reviews over the years and is excited about the opportunity to review movies.


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