Everydata: The Misinformation Hidden in the Little Data You Consume Every Day

by John H. Johnson, Mike Gluck

While everyone is talking about "big data," the truth is that understanding the "little data"--the stats that underlie newspaper headlines, stock reports, weather forecasts, and so on--is what helps you make smarter decisions at work, at home, and in every aspect of your life. The average person consumes approximately 30 gigabytes of data every single day, but has no idea how to interpret it correctly. EVERYDATA explains, through the eyes of an expert economist and statistician, how to decipher the small bytes of data we consume in a day. EVERYDATA is filled with countless examples of people misconstruing data--with results that range from merely frustrating to catastrophic: The space shuttle Challenger exploded in part because the engineers were reviewing a limited sample set. Millions of women avoid caffeine during pregnancy because they interpret correlation as causation. Attorneys faced a $1 billion jury verdict because of outlier data. Each chapter highlights one commonly misunderstood data concept, using both realworld and hypothetical examples from a wide range of topics, including business, politics, advertising, law, engineering, retail, parenting, and more. You'll find the answer to the question--"Now what?"--along with concrete ways you can use this information to immediately start making smarter decisions, today and every day.

Genres: Nonfiction, Science, Technology, Business, Audiobook, Psychology, Mathematics

224 pages, Hardcover

First published April 12, 2016

Reviews

[Philip · Rating 2 out of 5]

Maybe I'm a bit harsh on this book. The topic is important, both on the micro- and macro levels, and "Everydata" does a decent job covering some of the more obvious abuses and misunderstandings pertaining to data and statistics - things like mean v. median, correlation v. causation, p-values, sampling errors, etc.

However, it's just that, well, it's not a very fun read. The tone is doing its best to be off-putting, the information and the examples are pretty much as commonplaces as it gets (even though the authors present it as if it were groundbreaking - dudes(!), I think this is the third book I've read recently that discuss the Challenger disaster), and it's all kind of dull - frankly.

In short, the information is basic - if good and important - but there are better books out there covering the same info. I'd recommend picking one of them up instead.

[Jay French · Rating 2 out of 5]

This was not quite as deep as I had expected. This is roughly equivalent in math discussion to the high school business math level (which my daughter is currently taking, so I recognize some of this from recent homework help). The concepts, like forecasting and sampling, are all described very well, and in some cases with some interesting examples. It goes as deep as discussing chance when flipping multiple coins or changing scales on charts to tell different stories. This is so basic, and the description is so well done, that the sections talking about charts don’t really require seeing them. That’s rare for the books I’ve listened to (I partook of the audio version here) with visual content – audio versions usually don’t work describing visual mathematical concepts.

My favorite bit was what I now see is a popular quote from AC Nielsen Jr. on sampling -- "If you don't believe in random sampling, the next time you have a blood test, tell the doctor to take it all."

I can imagine this is a pretty good book for someone with no sense of how data is used. For me it was not a good use of time. I'll say it averages to an OK book.

[Daron Yondem · Rating 3 out of 5]

I found it very repetitive. The content is good tough.

[Koen Crolla · Rating 2 out of 5]

Patronising middle-school Statistics and cocktail party factoids. There's undoubtedly an audience that will find this book useful (very small children, Americans), and, unfortunately, a larger one that will find it interesting, but I, for one, am getting very tired of these deeply condescending booklets written by slimy white guys who think it's very important you realise they hold an advanced degree (though not usually one in a field related to the one they're writing about; John H. Johnson, PhD's Econometrics degree is better than most in that respect) and who think they're going to change the world with Freakonomics-style anecdotes.

[Josh Ferrara · Rating 4 out of 5]

A quick, helpful guide on how to interpret the data we all face every day. I want to give this book to everyone in my life.

We're all bombarded with data every day, but most of it has caveats or flaws that aren't immediately obvious. The authors of this have essentially created a field guide to help point out common ways that data can be manipulated, whether intentionally or not, to shape our interpretation.

Some of these were so validating to questions I've had in the past, and some of these were wildly insightful angles I've never considered. Ultimately I gained a number of new lenses to view and analyze data with.

Read this one if you want to be a more informed consumer, teammate, partner, and human.

[Skyler · Rating 3 out of 5]

Short, approachable book about statistics and interpreting data

[Chris Dziewa · Rating 3 out of 5]

This was a basic intro to understanding data around us. If you learn anything from this book hopefully it is that you should remain skeptical. I personally don't have time to study the research for every claim made online (I doubt most people do) as would be necessary to do based on this book, but it is important to realize that people often jump to their own conclusions about data either intentionally or not.

[Erikka · Rating 5 out of 5]

Mark Twain said there are three types of lies in the world: Lies, Damn lies, and Statistics. This book explores that quote a bit farther--what exactly makes statistics a lie? Is there a time when they are not lies? Of course. Are there times when they are intentional lies? Oh my yes. Are there times when they are unintentionally misleading? Most of the time, it seems. This book documents the different ways that a stat can go astray, what that means in your every day life, and how you can avoid these traps in consuming or creating your own statistics.

I am absolutely not a math person. I can't math. Don't be scared by the idea of statistics--there is not a single concept in this book that couldn't be easily understood by a high school student. They take complex concepts and make them into a fun, readable, and easily-applied set of rules and thoughts. The real-world examples are current, well considered, and do a great job of illustrating each point. I especially liked the sum-up at the end of every chapter, and the big sum-up at the end of the book.

We are constantly bombarded by data, every day, every minute. This book promises to make you a smart consumer of that data, and I think it lives up to that promise very well.

[Gwen · Rating 5 out of 5]

Are you easily swayed (or turned off) by chart-wielding political pundits? Do you tense up after reading yet another article about the latest medical research study? Whether you’re a confident or a “statistics-illiterate” consumer of information, Everydata: The Misinformation Hidden in the Little Data You Consume Every Day is a clear, enjoyable guide to decoding our data-driven world. You’ll understand the difference between mean and median, causation and correlation, as well as review concepts related to sampling, forecasting, and bias. Co-authored by an economist and a copywriter, this book breaks it all down, using highly relatable examples and generous dashes of humor to make the nuanced truth behind “definitive” data easier to uncover. In an era dominated by splashy infographics and clickbait-style headlines, Everydata’s balanced presentation is absolutely indispensable.

[Grant Barnes · Rating 3 out of 5]

Everydata = the data that surrounds us in our everyday lives

This book is a relatively simplistic overview of many principles of statistics such as, outliers, margins of error, statistical significance, sampling, cherry picking, confirmation bias, averages, probability, forecasts, etc.

The goal of the book is to make the reader a better educated consumer of “everydata.”

Notes:

A sample of the population in question is used and inferences are made on the whole when polling the entire population in question is not possible.

A striking feature of research in American psychology is that it’s conclusions are based not on a broad a cross-section of humanity but hey small corner of the human population; mainly person living in the United States.
The United States is less than 5% of the worlds population but his home to 68% of samples in studies.
But wait, there’s more; research is done consistently on college students, specifically undergraduate students in psychology classes.
According to one journal, 2/3 of the published studies done in America used undergrad psychology students for their samples.

Bad

Regarding sample size, bigger isn’t always better. You could sample every undergrad psychology student in the US and it wouldn’t give you a better picture of the US or world population as a whole.

For those who don’t like sampling:
“The next time you have a blood test, ask them to take it all out.”
-Arthur Nielsen, Sr.

5 things you can to do make better decisions using aggregated date, averages, and outliers.
1. know what a summary statistic is and what it isn’t; sometimes summaries don’t tell the full story.
2. Understand what type of average is being presented; mean, median, or mode. Example: the average person in the world has two arms (some have zero or one arm but very few have 3)
3. Ask, “An average of what?” Is it representative of a sample? Is it an average of averages?
4. See if all the data is treated equally. Weighted averages?
5. Identify outliers, understand the importance they can have on the data, and exclude where appropriate. (Ex Bill Gates lives on a street with 9 other people who earn $50K annually, their mean income would be over a $Billion)

Omitted variables are the main reason correlation does not equal causation.

5 things you can do to see if the data you’re seeing actually matters:
1. Ask if the results could be due to random chance (sample size)
2. Understand that many findings are based on probability.
3. Know that the data you see in headlines is often part a range (+/- confidence interval)
4. Even if the effect is statistically significant, look at the size or magnitude of the effect.
5. Consider the impact that data has on your life. Just because something is statistically significant and has a large magnitude it may not have an impact on your every day life.

How to be a smart consumer of data that is misrepresented or could be misinterpreted:
1. For charts and graphs take a good look at the X axis and Y axis. Scale and height can lead to misleading results.
2. Pay attention to the language; what people don’t say it often as important as what people do say.
3. Verify your source.
4. Make sure it’s not a mistake. Almost 1 in 5 large businesses have suffered a financial loss due to a spreadsheet error.
5. Interpret the data correctly. Sometimes the data is correct but it is misinterpreted because of confusion with fractions, decimals, or other user error. (1/3lb burgers don’t sell nearly as well as 1/4lb burgers because most people think 1/3 is smaller than 1/4)

“4 out of 5 doctors recommend Gerber baby food.”
So, only one didn’t recommend Gerber, right? Wrong. Actually, only about 12% of doctors surveyed recommended Gerber. How did they get to four out of five? Cherry picking. Cherry picking means you select anecdotal data to make your point while ignoring other data points that contradict it. At the time of this survey, many doctors (more than 25%) did not recommend baby food at all because of added sugar and fillers.

Here’s the actual data:

562 pediatricians surveyed
408 recommend baby food
76 recommended a specific brand
67 recommended Gerber

Based on the data, the following statements are true and give a more accurate accurate representation of the findings:
“Of the doctors we polled who recommended baby food, less than 1 (0.8) out of 5 recommended Gerber”
“Of doctors who recommend baby food, and of those who recommend a specific brand of baby food, 4/5 recommend Gerber”
“1 out of every 10 doctors we polled recommended Gerber”
...but none of those sell as much baby food as the cherry picked line.

The FTC (Federal Trade Commission) ruled that Gerber was misleading with cherry picked data.

How to be a smart consumer of forecasts:
1. Know that predictions of the future depend upon the past. If there are issues with the data of the past, it will negatively affect the accuracy of your forecast.
2. Understand the difference between deterministic (it will rain tomorrow) and probabilistic (there’s a 40% chance if rain) forecasts.
3. Understand the terminology; “forecast” and “prediction” are often used interchangeably, but they are different.
4. Understand that the accuracy of a forecast may change over time. Forecasting the final score of the baseball game in the seventh-inning would surely be more accurate than at the beginning of the game. More data.
5. Accept that there will always be some level of uncertainty.

In summary, five takeaways to be a good consumer of everydata:
1. Recognize data when you see or hear it. It’s everywhere.
2. Get your facts right. Verify the data you’re seeing is in fact accurate.
3. Understand where the data is coming from and who is presenting it (biases).
4. Watch out for the obvious data traps, e.g., correlation vs. causation.
5. Understand that interpreting data correctly will help you make good decisions.

[Annie · Rating 4 out of 5]

The book is more about statistical concepts than making use of daily digital data. An example used in the book is the explosion of the Challenger, which happened in 1986. That's not a current event... has nothing to do with "big data" or "little data." It explained how the use of selective data led to the unanticipated disaster. The book is useful for readers who aren't familiar with statistics and want to learn. One story that fits with the title of the book is about Zillow, the website that gives "Zestimate" for the value of a house. People like to look at one number and think that is the answer. But they don't realize that an estimate is just that -- an estimate, which also includes a margin of error. That means some Zestimate will be on target, some above, and some below the actual value of the house.

[David Webber · Rating 1 out of 5]

Full of trite and tired examples of the misuse of data and statistics, particularly as reported in the news, although the author still can't seem to conjure up recent examples - the Space Shuttle Challenger disaster is given as one example. Only recommended if you don't have even the most basic understanding of probability and statistics and desire basic discussions of causation vs. correlation, margins of error, etc. There are a few good examples in the book that are worthy of discussion, so I guess I'd give it 1-1/2 stars.

[Jill Dater · Rating 4 out of 5]

For a person who doesn't exactly like to read about numbers and has never taken a stats class, this was pretty readable. In particular, I like what he (they?) had to say about media's manipulation of data.

[Brett · Rating 2 out of 5]

A good book about statistics for a layman. However, it is written in a strange friendly tone. Not an authoritative one. I was removed from the prose many times due to inline witticism.

[Lindsey Clewett · Rating 4 out of 5]

This is a 3 * book for me since there is nothing new here for someone who is literally paid to interrogate data. But for the audience at which it is aimed i.e. everyone else this is gold dust. Full of information to help you critically analyse headlines and click bait, this book should be required reading in schools. This is very easily digestible and whilst the title might put some people off, and they may think they have no interest in data, and therefore no need for this book, we ALL consume data every day, through newspaper and TV headlines, social media, in conversations with friends, at work, in literally everything we do and everywhere we go. This book is not about actual data, or statistical analysis, but it is about understanding certain aspects of analysis and why you should question everything. It covers the difference between correlation and causation, population sampling, unknown variables, margins of error, probability, and statistical significance, and whilst this is not a textbook on how to calculate these things, what it does do is outline what each of them is and why, if you don't know these facts about a particular study, you should take what the media are telling you about it with a pinch of salt. Full of interesting case studies to help illustrate the point, this book will help you become a discerning and critical consumer of data.

[Casey · Rating 4 out of 5]

A good book, providing a primer for understanding the gathering, assessment, and presentation of data. The authors, marketing consultant Mike Gluck and economist John Johnson, deliver a very broad overview on data science and statistics. The authors promise to answer the ‘now what’ questions of a person in receipt of muddled or raw day. In this short and very focused work they introduce the reader to basic data interpretation skills. With its engaging style and use of real world examples, this book is more than a mere reference manual. But, it lacks (and, to be fair, doesn’t promise to provide) concepts or proposals to help an organization become more data oriented. Instead, it is a good explainer for an individual, without a quantitative background, seeking a lifeline of understanding in our increasingly data centric world. Highly recommended for anyone seeking to increase their individual data literacy.

[Anand Gurunathan · Rating 3 out of 5]

The book does a good job on two fronts:
1) How media/research journals provide us (consumers) a narrow view of the data to sensationalize the news or to push their agenda
2) Using copious examples, the authors do a good job explaining statistical terms and how to interpret data when it's presented to us.

If you're fairly new (in your professional life) to handling data on a regular basis, you may find the book useful. If you've been in the corporate world for a while, you already know how the game is played :)

[Rodney Hall · Rating 4 out of 5]

“Everydata” provides an easy to read and understand guide to better informed interpretation of everyday claims and the data behind them. It is especially helpful to anyone without a significant background in statistics.

I recommend this book to anyone seeking to be better prepared when considering claims from politicians, the media, special interest groups, or other sources with an agenda. The book provides a solid foundation for questioning narratives that may be based on misleading information - both accidental and intentional.

[Ilinca · Rating 3 out of 5]

If you've read The Signal and the Noise, Naked Statistics, Damned Lies and Statistics or any of the many similar titles, you really don't need to read this one, too. I also listened to it read by the author, which, in 99.9% of the cases, is a terrible idea, and JJ is a terrible narrator. If you've never read any of the other books, it's interesting; If you have and/or are any good at interpreting results and challenging statistics, you can give it a miss.

[Lalit Pamnani · Rating 5 out of 5]

It's a good book. As title says, it explains lots of gotchas about the data we consume everyday.
Author spends good amount of time explaining how we mix Correlation with causation, with tons of everyday example.
Book doesn't go deep dive on data or stats for nerds so if you expect that, then it might disappointed you little. But in a way, it's a good thing that author explained things in plain English and doesn't go deep so everyone can understand.

[Rachel · Rating 2 out of 5]

Maybe 2.5 stars if it has been awhile since you used statistics and graphs and want a refresher.

This book dove into how to read and understand statistics as well as a discussion in cherry picking data. All examples are based on real examples - sports, advertising, politics, media articles, etc.

Bottom line: understand what the data behind the statistics is and who is presenting it. Ask questions and don't take everything at face value.

[Karen Briscoe · Rating 5 out of 5]

There is information hidden in the little data one consumes every day. It is that EVERYDATA that contains powerful information. This book will help you make smarter and faster decisions. It is filled with countless stories and examples of how data can help and can cause unintended consequences. Karen Briscoe, author and podcast host "5 Minute Success"

[Melinda · Rating 3 out of 5]

I think this could have been a really good book, but somehow it lacked the right tone. I listened to this as an audiobook, and it just seemed a bit too 'friendly'? I think this book gave some first level insights into this topic, good for laypeople, but just didn't give me the depth I needed.

[Jared Fritz · Rating 3 out of 5]

Only got through half of book. Had high hopes after hearing the author(s) interviewed on a podcast, but I would much rather re-read a Freakonomics book or The Signal And The Noise than finish this one.

[Emma Godfrey · Rating 5 out of 5]

This is a fantastic and accessible book to help everyone ask more questions about the “studies” used to influence your behavior. Also, an excellent overview of common statistical pitfalls and concepts. Filled with examples and anecdotes, it’s informative and entertaining.

[Marcos d Ornellas · Rating 4 out of 5]

A must read book for everyone.

[Erik Abelsson · Rating 2 out of 5]

Was expecting more. Gave some nice anecdotal arguments that could be used when discussing data. Nothing for someone who has at least some statistical schooling.

[Tarik Malek · Rating 4 out of 5]

Good introduction into statistics for the layman.

[Jason · Rating 3 out of 5]

Great book, nothing that hasn't necessarily been written elsewhere but an excellent primer.

[Sam Motes · Rating 4 out of 5]

Summary of this read: lees figure and figures lie. The author challenges you to question how data was collected and the motivation of the presenter of the data before taking it as the truth.