“Shark attacks in 2010 rose 25%!” screamed one headline. Below it was a picture of a sign posted at a tourist spot in the U.S.: “WARNING: Recent shark attack. Beach closed.”
You all remember the summer of Sharkmageddon. There was an epidemic of shark bites on swimmers. Well, there was an epidemic of stories, anyway.
As you should know by now, numbers can lie just like pictures can lie. Here’s the truth about the latest summer of Jaws. There were 79 attacks in 2010, up from 63 a year earlier. That is indeed a 25% jump. But you really don’t care what the number of attacks was – the real question is: What are the odds that you will get chomped? Are the world’s oceans really 25% more dangerous? We’ll ignore the fact that 5 of the attacks in 2010 were the result of two angry sharks that had a human-eating orgy during a four-day stretch that year. Let’s just put the data into a different context.
There are about 3 billion people on the planet. Let’s say on any given day 1% of them swim … forget it, let’s say in any given year, 1% of them swim in an ocean or body or water that might have a shark. That’s 30 million people. Let’s say these swimmers average five swims per year. That’s 150 million treks into shark-infested waters.
2009: 0.0000042 attacks per swim (63/150 million)
2010: 0.0000053 attacks per swim (79/150 million)
In other words, 99.99958% of swims were shark-attack free in 2009, vs. 99.99947% in 2010. That’s not really a 25% increase in risk, is it?
Risk-assessment is one of the most critical ways that humans process data, and we do it constantly. Is it too risky to drive in this snowstorm? Is it too risky to sleep with this person you met at the bar? Is it too risky to eat that leftover meat that’s been in the fridge for four days? The problem is, most humans are terrible at making risk calculations, constantly choosing emotions over data. Far, far more people die driving to the airport than flying in an airplane, yet you don’t hear much about airport taxi phobias, do you?
Even worse, we live in the age of Big Data, when single slices of facts are thrown around like Gospel, when in fact they contain more lie than fact. Executive suites and conference rooms alike are teeming with “Shark Attack”-like pronouncements from spreadsheet-wielding analysts who believe that only items worth counting are worth doing. That’s one reason customer service has suffered so greatly in our time — it’s a lot easier to count the number of minutes that agents spend on the phone than the way consumers feel after an interaction.
Bad risk analysis is really the function of bad data, which falls under the category of Data Idolotry in the eight reasons people get stuck. Workers are often afraid of taking the leap towards a new job, or a new career, but don’t realize that sitting still carries with it even more risk.
There’s plenty of reasons that people make ineffective choices over and over again. They get bad information. They get limited information – using only a scale to assess health, for example. Sometimes, they poorly weight the information – a doctor says have surgery, a neighbor says try drinking herbal tea every night for a month, and they consider both these inputs equally. And then, there’s the most common problem: we react poorly to information we receive. We choose to ignore critical information that tells us we’ll likely be laid off within six months, or that a new competitor has improved on our product and is stealing our customers. Here are five categories of data idolatry and poor risk assessment. The first, ignoring measurements, is probably killing your retirement savings. Here’s why:
If the stock market collapse of 2008 proved anything, it’s this: We stink at assessing risk. Not just Americans; everyone. There’s no more direct, comprehensive or scientific experiment we could hope to conduct. Most of the world made bets, consciously or unconsciously, and most of us lost. In fact, we lost 45% of the world’s wealth, countless trillions of dollars. That’s quite a doozy.
Of course, the world is full of proof that we are terrible at risk assessment. We fear sharks but don’t wear condoms — or wash our hands. We’re terrified of turbulence but drive drunk. We subject grandmas to full-body frisking at the airport, but let terrorists on watch lists get onto airplanes. We buy lottery tickets, often described as a tax on the mathematically disinclined, despite knowing the odds.
Maybe you are above all that. And maybe you feel like you could blame all those market losses on other things – a lack of information, terrible advice, even criminal behavior. Those are perfectly good explanations, but they prove our point. We incorrectly assessed the risk that criminals giving bad advice and hiding data controlled the markets. We can do better. In fact, we have to.
These primal skills, sadly, have remained relatively primal. Brain studies show that when humans encounter risks, what’s sometimes called the “reptilian” portion of our brains take over. The reptilian brain controls base functions we have in common with most of the evolutionary world – fight or flight responses. It was designed to optimize our ability to run away from dinosaurs in a crisis. Sadly, when faced with decisions about things like money and investing, many people’s brains believe they are being chased by a dinosaur and act accordingly. That is, they run. Metaphorically speaking.
Our reptilian brains might be useful at assessing a trip down a double-black-diamond ski slope. But we’re relatively awful at assessing the risk of a mutual fund, inflation or trusting our current place of employment. As a result, many people are overly risk-averse, not realizing how risky that is.
The reptilian brain, as you might imagine, is about as good with subtlety as a Gila monster. It can muster two thoughts: BUY and SELL. QUIT or STAY. It definitely can’t come up with the advantages of dollar-cost averaging in a falling market. OK, it usually comes up with one additional thought when it comes to money: PUNT! They find a money manager, and they say, Please, Mr. Man in a White Shirt – take care of this for me so I don’t have to think about it.
And trust us, Mr. Man in a White Shirt is happy to take your money while making sure you can’t live without him for the rest of your financial life. He’s going to send you long paper statements you can’t really understand. Sometimes these statements will show your pile of gold is larger, and you’ll be happy. Sometimes, it will shrink, and your reptilian brain will yell, “SELL!” You’ll call, and he will calm down the reptile within you – that’s his main skill, speaking to that part of your brain. And you’ll decide to BUY again. You’ll make that decision over and over, which really is the definition of insanity. It’s also the definition of a plateau.
Or, you’ll do what most Americans do and simply stop opening the monthly statements. Somewhere, someone told you (probably that Man in a White Shirt) that the best thing to do with your investments is to ignore them, to “set and forget” the money you’re putting in your retirement account. Sometimes, your retirement planning can be one of those background tasks we discussed in the introduction. However, usually, it’s not a good idea to punt on your future like that. But again, “Don’t worry about it. I’ll take care of it,“ is a secret message aimed directly at your reptilian brain. Just ignore it and everything will be fine! Can you imagine the gall of this advice? Thank God our ancestors didn’t fall for this trick. Imagine if no one ever checked to see if insects were devouring the winter grain stored in the grain elevators!
Back in 2008, a majority of U.S. adults had put their money into set-and-forget mode. Clearly, that didn’t work. How do you break free from this reptilian, all-or-nothing, buy-or-sell cycle? By measuring things, and listening to what the measurements tell you. Try managing some of your own money for a while, depositing it into an S&P 500 index fund, and compare that to the performance of the Man in a White Shirt over time. If you are doing better, than you really need to fire him. Real data is the key to moving the decision out of the part of your brain that made you choose whether you should or shouldn’t jump off that high wall outside your elementary school when you were 8 years old.
Data is hardly infallible, however. It needs to be placed in proper context. It needs to be interviewed with hard questions. Otherwise, it simply creates a hall of mirrors. So here are ways that data can fool you.
1. Counting the Wrong Things
Why do airlines mess up one planeload’s entire day when a single flight has a mechanical problem, rather than simply shift everyone one flight later? Because the FAA rewards that behavior. On-time performance is all about percentage of on-time flights. That’s why it’s OK to screw over hundreds of passengers, delaying them 24 hours or more. When you create a metric, people often simply conform to that metric. That’s why so many of us are ineffective. It’s why call centers reward the speedy over the effective. When you are deciding what to do with your money, or your career, the markers you pick are just as important as the goals you pick.
2. Opportunity Cost
Give most people a choice, and they will pick a known unpleasant experience over an unknown every time. Many folks are very bad at measuring what they are giving up whenever they make choices. Go on vacation here, and you can’t go there. Take this job, and you can’t take that one. Stay in place at work and…you’ll never have an adventure. The way to avoid asking yourself “what might have been” is to carefully think about “what might be.”
3. Magical Thinking
Remember that day you hit every light just right and got to work in 16 minutes? Well, that day is not today. In fact, it may never happen again. Yet you still leave with only 16 minutes to go to work. Why? You are a victim of magical thinking. Many folks get stuck – or they are perpetually late — because they can’t help being overly optimistic in all their endeavors. Magical thinking leaves us wondering why one-hit wonders can’t repeat their success. Never forget: Luck is quite a real phenomenon. Statisticians call it an “outlier.” Chasing after an outlying event will leave you chasing your tail.
4. Overweighting Grandma
We used to joke that news is what happens to your editor on the way to work. No matter what reporters say, if an editor’s block has potholes, the town has a major pothole problem. Everyone is like this. If your grandma says at dinner that there’s too much color pink in TV these days, you’ll suddenly shy away from using pink in your next design. It’s human nature. A similar quality is called “recency.” You might read 27 reports going into a big meeting, but if someone says something to you as you walk into the room, you will give that person’s thought equal weight to all those reports.
5. Accidental Reinforcement
Imagine you’ve been put a rat in a trap maze. If the rat solves the maze, she gets a piece of food dispensed from above. The idea here is to teach the rat to solve the maze as quickly as possible. But let’s say you work in a lab that’s particularly hard-hit by funding cutbacks, and your rate maze hasn’t been updated since the 1960s. It’s been acting up lately, but you have a test to run, so you drop the critter into the maze. She’s skittish, and so the first thing she does is panic and bang into a maze wall. Your aging trap shakes, and your food dispenser erroneously delivers a food pellet right where the rat is now seeing stars.
What happens next? The rat jealously scoops up the crumb, devours it with satisfaction, and then starts banging her head into the wall, expecting more food. You try explaining, cajoling, begging, even picking the rat up and putting her at the end of the maze, where more food awaits. No matter. She learned quickly that banging walls = food, and she’s going to keep on trying that, again and again. This is the torture called accidental reinforcement.
Often, an early success is the worst thing that can happen to a person.
If you, like me, enjoy data, then you probably realize that many of these maladies are SOLVED by GOOD data. Understanding that grandma’s observation represents a small sample size, you can burn out your observational bias with good data. However, good data is hard to find, and I hope you also recognize that in nearly all these flaws, dirty data is to blame. Given that we rarely have access to great data when making life choices, maintaining a healthy skepticism to the data you do get is essential to avoid falling into the data idolatry trap.
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