This is a very good book providing much needed insights to understand the flood of statistics that are available online everywhere today from news organizations to non-profits, and especially marketing firms representing various clients.
Tim certainly presents solid lessons addressing today’s numerous infographics that dominate many webpages and blogs.
This can certainly viewed as an updated version of How to Lie with Statistics by Darrell Huff. Ultimately, Tim wants readers to better understand data from the world that is pushed into our browsers everyday.
In this book, readers will understand truth with statistics. Tim provides ten rules to interpret statistics. Presenting data without understanding the ground rules can (and often) misdirects the reader. Sometimes on purpose.
In addition, Tim provides great insights and stories that help reveal how to overcome types of statistics that play upon one’s emotional intelligence.
Below is a short view of each chapter presented as rules that help readers understand statistics:
Rule 1: Search your Feelings
First, stop and notice our emotional reactions to a claim, rather than accepting or rejecting it because of how it makes us feel. Really, stop and focus on how new information makes you feel. Our emotions certainly influence us, and we may dismiss statistics because we immediately do not like them. We can find ourselves embracing false statistics because we do like them. In our seemingly crazy world of 2021 (and 2020) this is so important as organizations and speakers pin-point emotional data to trigger our beliefs, opinions, and actions.
Rule 2: Ponder your personal experience
Our personal experiences (worm’s eye) does not seem in sync with statistical data (bird’s-eye). Tim is suggesting we look to combine the statistical perspective with our own personal experiences. For example, you ride public transit (bus, subway, or train) in a large city, ridership is seemingly only half-full. However, riding during rush hour is another story. Both perspectives help you understand the truth.
Rule 3: Avoid Premature Enumeration
Learn to look at the labels on the data. This is just like reviewing ingredients on food. We need to ask if we really understand the data. So, make sure your understanding is accurate. In fact, when counting beans, the definition of a bean matters:
Statisticians are sometimes dismissed as bean counters. The sneering term is misleading as well as unfair. Most of the concepts that matter in policy are not like beans; they are not merely difficult to count, but difficult to define. Once you’re sure what you mean by “bean,” the bean counting itself may come more easily. But if we don’t understand the definition, then there is little point in looking at the numbers. We have fooled ourselves before we have begun.
pg. 126-127
Rule 4: Step Back and Enjoy the View
Seek out comparisons and context, putting any claim into perspective. Finding the right information places a statistic into context. So be alert for trend data that is misused.
Tim actually provides a very compelling example. Londoners found this headline in February 2018 splashed across the headlines: London’s Murder Rate Is Higher Than New York’s for the First Time Ever! There were fourteen murders in New York City, but fifteen in London:
Here are a few facts worth knowing about murders in London and New York. London had 184 murders in 1990, while New York suffered 2,262—more than ten times as many. It’s with that image in mind of New York as a murderous place that Londoners are alarmed at the idea that they might have become as rotten as the Big Apple. But London’s murder rate has fallen, not risen, since 1990. In 2017, there were 130 murders in London, including ten people killed in terrorist attacks. London was safe in 1990 and it’s a little bit safer today. As for New York, murders fell to 292 in 2017. That means New York is still more dangerous than London, but much, much safer than in 1990.
pg. 126-127
Not so fast! Tim helps us find the error in this misleading data.
Rule 5: Get the Backstory
Learn where the statistics came from and certainly understand what data vanished into obscurity.
Rule 6: Ask Who is Missing
Ask who is missing from the data. Would our conclusion differ if they were included? This seems similar to Rule 5, however consider for example he historical underrepresentation of women in clinical trials. Makes perfect sense to data outcomes.
Rule 7: Demand Transparency when the computer says No
Ask tough questions about algorithms and the big datasets that drive them, recognizing that without intelligent openness they cannot be trusted. This can be a very effective rule, however it requires a very through awareness of algorithm misuse in the past. Cathy O’Neil’s book Weapons of Math Destruction is a primer that should be read to understand and invoke this rule.
Rule 8: Don’t take Statistical Bedrock for Granted
US politicians for decades certainly attack reports from the Office of management and budget. For a good reason, as OMB data simply tells the truth. Politicians simply lie every day. So, respect the statisticians who are performing heroic work. And protect them especially in today’s climate.
Rule 9: Remember that misinformation can be beautiful, too.
Remember that beauty is only skin deep. Don’t let the beauty mislead you also applies to beautiful graphs and charts.
Rule 10: Keep an open mind
Finally, ask how we might be mistaken and whether the facts have changed is just as important.
In conclusion, Tim is certainly delivering an amazing book filled with important rules to understand statistics. This is a must read book for everyone!
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