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Christmas – Deadliest Day of the Year
Drop by any time.
Here’s a little exercise in critical thinking. More people in the United States die on Christmas Day than on any other day of the year. It’s our deadliest day. New Year’s Day is the second deadliest day.
The first question a critical thinker would ask is: Is that really true? Here’s the evidence: an article published in Social Science and Medicine in October 2010, that analyzed 54 million death certificates from 1979 through 2004. (Click here for the abstract and charts; the full-text is behind a pay wall). Regardless of the setting or the cause, the number of deaths clearly peaks on Christmas Day. This affects all demographic groups, except children.
The next question a critical thinker might ask is: Why would that be? Here the logic gets a little fuzzy. As the authors of the research paper point out, they tested nine different hypotheses but believe more research is necessary. So let’s think about it a bit.
- Hypothesis 1: Perhaps it’s because people overeat on Christmas Day, overloading the digestive system, causing systemic stress and death. Really? One big meal causes death? If that’s the case, many of us would be long gone already.
- Hypothesis 2: It’s the stress of having all those family members and in-laws around. True, that’s a lot of stress but a lot of other holidays cause stress as well. If that’s the case, why wouldn’t we also see spikes on Thanksgiving or July 4th?
- Hypothesis 3: maybe sick people hang on until Christmas and then let go. It’s possible — people can and do keep themselves alive until a big event. But that doesn’t explain why mortality rises in the days and weeks before Christmas. If people were hanging on, you would expect to see a dip in deaths just before Christmas.
The hypothesis I like — which I spotted in the Daily Beast (click here) — is that Christmas isn’t abnormal in terms of life-threatening incidents, but is abnormal in the way people behave when a life-threatening incident occurs. If you feel chest pains on any random day, you may just head straight for the hospital. That’s a good idea because the sooner you get there, the better your chances of survival. On Christmas, however, people may delay, not wanting to spoil the festive atmosphere or leave the family celebration. They may also believe that they’ll get poor service at the hospital on Christmas. The hospital will likely be understaffed or staffed by second stringers, etc. Better to wait ’til tomorrow to get better service.
The next question a critical thinker might ask is: If this is true, what should we do about it, if anything? This hypothesis, of course, is not fully tested. We can’t claim conclusively that it’s true. But there is a certain logic about it. Perhaps enough that we can make Pascal’s wager — the evidence isn’t conclusive but it’s strong enough to make a bet. If we’re wrong we don’t lose much. If we’re right, we can save a lot of lives. So, what do we do? Perhaps we can advertise the phenomena and encourage people to get to the hospital quickly, even if it is Christmas. In fact, consider this article a public service announcement. If you have chest pains today, get your butt to the hospital pronto!
Merry Christmas!
Are You More Creative When You’re Sleepy?
Gosh, I’m feeling so creative.
I’m a morning person. I wake up every day full of plans and optimism. I just know I can solve the problems of the world today. (Yes, I’m a bit obnoxious). In the evening, on the other hand, I run out of gas. I like to do a little light reading and go to bed early. A psychologist would say that the morning is my “optimal” time while the evening is my “non-optimal” time.
It seems logical that I would be more creative during my optimal time, no? Well, … maybe not. According to two psychology professors, Mareike B. Wieth and Rose T. Zacks, your non-optimal times may be your better times for creativity.
In a research paper published in 2011 (click here or see full citation below), Wieth and Zacks determined the optimal times — morning or evening — of 428 randomly selected students and then asked them to complete, three “analytic” problems and three “insight” problems. Analytic problems “…require the solver to ‘grind out the solution’ by searching through and narrowing the problem space.” In other words, you start on a path and stay on that path until you find the solution.
Insight problems, on the other hand, “are often solved suddenly with a ‘flash of illuminance’ … or what has also been called an ‘‘Aha’’ experience where the solution seems to just pop into mind.” The process of solving an insight problem is also different. People typically start on a given path, hit a wall, and then jump to a different path. As the authors phrase it, “…to move past the impasse, the solver must break away from his or her focus on the current representation of the problem and find an alternative way of structuring the problem space.”
Students completed their six problems at randomly assigned times. Some completed the problems during their optimal times, others during their non-optimal times. The results varied by problem type. Students solved analytical problems better when they worked during their optimal time. For insight problems, however, students were more successful when they worked during their non-optimal times.
Why would that be? Wieth and Zacks hypothesize that it has to with “…inhibitory processes [that] control the flow of information from thought and perception.” Simply put, we can focus better during our optimal times because our inhibitory processes block out distracting information. That’s good for grind-it-out problems — our inhibitory processes help us focus on the solution path. With insight problems, on the other hand, distracting information can actually help us jump to the right path. During our non-optimal times, our inhibitory processes are less effective. We’re less focused and our mind wanders more. More “distracting” information enters our thoughts. All of that helps us discern other paths that can lead to an Aha experience.
So, do you want to be more creative? Just stay up late and let your mind wander. That’s not so hard.
Mareike B. Wieth & Rose T. Zacks (2011): Time of day effects on problem solving: When the non-optimal is optimal, Thinking & Reasoning, 17:4, 387-401. This work was supported by National Institute on Aging Grant R37 AG04306.
Does Smoking Cause Cancer in Humans?
You can’t prove nothing.
Let’s do an experiment. We’ll randomly select 50,000 people from around the United States. Then we’ll assign them — also randomly — to two groups of 25,000 each. Let’s call them Groups X and Y. We’ll require that every member of Group X must smoke at least three packs of cigarettes per day. Members of Group Y, on the other hand, must never smoke or even be exposed to second hand smoke. We’ll follow the two groups for 25 years and monitor their health. We’ll then announce the results and advise people on the health implications of smoking.
I’ve just described a pretty good experiment. We manipulate the independent variable — in this case, smoking — to identify how it affects the dependent variable — in this case, personal health. We randomize the two groups so we’re sure that there’s no hidden variable. If we find that X influences Y, we can be sure that it’s cause and effect. It can’t be that Y causes X. Nor can it be that Z causes both X and Y. It has to be that X causes Y. There’s no other explanation.
The experimental method is the gold standard of causation. It’s the only way to prove cause and effect beyond a shadow of a doubt. Yet, it’s also a very difficult standard to implement. Especially in cases involving humans, the ethical questions often prevent a true experimental method. Could we really do the experiment I described above? Would you be willing to be assigned to Group X?
The absence of good experiments can confuse our public policy. For many years, the tobacco industry claimed that no one had ever conclusively proven that smoking caused cancer in humans. That’s because we couldn’t ethically run experiments on humans. We could show a correlation between smoking and cancer. But the tobacco industry claimed that correlation is not causation. There could be some hidden variable, Z, that caused people to take up smoking and also caused cancer. Smoking is voluntary; it’s a self-selected group. It could be — the industry argued — that whatever caused you to choose to smoke also caused your cancer.
While we couldn’t run experiments on humans, we did run experiments on animals. We did essentially what I described above but substituted animals for humans. We proved — beyond a shadow of a doubt — that smoking caused cancer in animals. The tobacco industry replied that animals are different from humans and, therefore, we had proven nothing about human health.
Technically, the tobacco industry was right. Correlation doesn’t prove causation. Animal studies don’t prove that the same effects will occur in humans. For years, the tobacco industry gave smokers an excuse: nobody has ever proven that smoking causes cancer.
Yet, in my humble opinion, the evidence is overwhelming that smoking causes cancer in humans. Given the massive settlements of the past 20 years, apparently the courts agree with me. That raises an intriguing question: what are the rules of evidence when we can’t run an experiment? When we can’t run an experiment to show that X causes Y, how do we gather data — and how much data do we need to gather — to decide policy and business issues? We may not be able to prove something beyond a shadow of a doubt, but are there common sense rules that allow us to make common sense decisions? I can’t answer all these questions today but, for me, these questions are the essence of critical thinking. I’ll be writing about them a lot in the coming months.
Ice Cream and Muggings
Feel like mugging someone?
Did you know that the sale of ice cream is strongly correlated to the number of muggings in a given locale? Could it be that consuming ice cream leads us to attack our fellow citizens? Or perhaps miscreants in our midst mug strangers to get the money to buy ice cream? We have two variables, X and Y. Which one causes which? In this case, there’s a third variable, Z, that causes both X and Y. It’s the temperature. As the temperature rises, we buy more ice cream. At the same time, more people are wandering about out of doors, even after dark, making them convenient targets for muggers.
What causes what? It’s the most basic question in science. It’s also an important question for business planning. Lowering our prices will cause sales to rise, right? Maybe. Similarly, government policies are typically based on notions of cause and effect. Lowering taxes will cause the economy to boom, right? Well… it’s complicated. Let’s look at some examples where cause and effect are murky at best.
Home owners commit far fewer crimes proportionally than people who don’t own homes. Apparently, owning a home makes you a better citizen. Doesn’t it follow that the government should promote home ownership? Doing so should result in a safer, saner society, no? Well… maybe not. Again, we have two variables, X and Y. Which one causes which? Could it be that people who don’t commit crimes are in a better position to buy homes? That not committing crimes is the cause and home ownership is the result? The data are completely tangled up so it’s hard to prove conclusively one way or the other. But it seems at least possible that good citizenship leads to home ownership rather than vice versa. Or maybe, like ice cream and muggings, there’s a hidden variable, Z, that causes both.
The crime rate in the United States began to fall dramatically in the early 1990s. I’ve heard four different reasons for this. Which one do you think is the real cause?
- Legalized abortion — in 1973, the Supreme Court effectively legalized abortion in the United States. Eighteen years later, the crime rate began to fall precipitously. Coincidence?
- The “broken windows” theory of policing — police traditionally focused on serious crime while ignoring petty crimes. In the 1980s, sociologists began to argue that ignoring petty crime sent a signal to would-be criminals that citizens will tolerate crime in a given area. Even minor crimes like broken windows could send the wrong message. Police adopted the idea and started cracking down on petty crimes. The message? If minor crimes are not tolerated, just think what they’ll do for bigger crimes!
- The aging population — we’re getting older. Young people commit a disproportionate number of crimes, especially violent crimes. As our nation ages, we become more sedate.
- The “get tough” sentencing movement — politicians in the 1980s began to sponsor legislation to “get tough” on crime by imposing longer, mandatory sentences. One result has been a dramatic rise in our prison population. (In fact, I read recently that the U.S. has 700 people incarcerated for every 100,000 citizens. In Sweden, the equivalent rate is 70 prisoners. Could it be that we’re ten times more criminal than Swedes? Swedes are blonde and they don’t commit crimes. Cause and effect, right? Perhaps we should all dye our hair blonde.)
Which of the four variables actually caused the declining crime rate in America? A lot is riding on the answer. Unfortunately, the data are so tangled up that it’s difficult to tell what causes what. But here are some rules for thinking about correlation and causation:
- If you think X cause Y, always ask the reverse question. Is it possible that Y caused X?
- Always look for a hidden variable, Z, that could cause both X and Y.
Actually, the only way to prove cause and effect beyond a shadow of a doubt, is the experimental method. Which leads us to our question for tomorrow: does smoking cause cancer in humans?
More Thumb Thinking
Us versus them.
Remember heuristics? They’re the rules of thumb that allow us to make snap judgments, using System 1, our fast, automatic, and ever-on thinking system. They can also lead us into errors. Last time I wrote about heuristics (click here), we looked at three of the 17 different error categories: satisficing, temporizing, and availability. Let’s look at four more today.
Affect — what’s your first response? What’s your initial impression? What does your gut tell you? These are all questions about your affect heuristic — more commonly known as gut feel. System 1 usually has the first word on a decision. If you let System 1 also have the last word on the decision, you’re making an affect-based decision. It may be a good decision — or maybe not. If you want to double check the accuracy of your affect, you need to fire up System 2. People with “poor impulse control” often stick with System 1 only and don’t engage System 2.
Simulation — if it’s easy to imagine a given outcome, then it’s more likely that outcome will occur, right? Not necessarily. At least in part, it depends on how good your imagination is. Salespeople can use simulation to very good effect: “Imagine how you would feel in this new suit.” “Don’t you think it would be great to drive a car like this?” “Imagine what other people will think of you when they see you on this motorcycle!” Simulation simply invokes your imagination. If it’s easy to imagine something, you may convince yourself that it’s actually going to happen. You could be right or you could be a victim of wishful thinking. Before you make a big decision, engage System 2.
Representation — “She looks like my ex-girlfriend. Therefore, she probably acts like my ex-girlfriend.” You notice that there’s a similarity between X and Y on one dimension. Therefore, you conclude that X and Y are similar on other dimensions as well. You’re letting one dimension represent other dimensions. This is essentially a poor analogy. The similarity in one dimension has nothing to do with similarities in other dimensions. Generally, the more profound a similarity is, the more likely it is to affect other dimensions. Physical appearance is not very profound. In fact, it’s apparently only skin deep.
Us versus Them — “The Republicans like this idea. Therefore, we have to hate it.” Unfortunately, we saw a lot of this in our recent elections. In fact, politics lends itself to the us versus them heuristic — because politics often boils down to a binary choice. Politics is also about belonging. I belong to this group and, therefore, I’m opposed to that group. This is often referred to as identity politics and is driven by demonstrative (as opposed to deliberative) speeches. In warfare, the us versus them heuristic may be good leadership. After all, you have to motivate your troops against a determined enemy. In politics, on the other hand, it smacks of manipulation. Time to fire up System 2. (For my article on demonstrative and deliberative speeches, click here).
Do you see yourself in any of these heuristics? Of course you do. All of us use heuristics and we use them pretty much every day. It’s how we manage “reality”. Unfortunately, they can also trick us into mistakes in logic and judgment. As you become more aware of these heuristics, you may want to engage System 2 more frequently.
To prepare this article, I drew primarily on Peter Facione’s Think Critically. (Click here)
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