When Outcome-Oriented Thinking Works–and When It Doesn’t

Roulette Wheel

Outcome-oriented thinking is focusing on the result, rather than the process for achieving the result. Over the last few years, outcome-oriented thinking has become increasingly important to my decision process. It interests me because much of the time, outcome-oriented thinking is extremely valuable, while at other times, it’s actually leads you down incorrect paths.

Removing bias

Outcome-oriented thinking is great because it often avoids many of the biases that seem to creep into process-oriented thinking. Process-oriented thinking focuses on building a logical chain from actions to results. It relies on the idea that the world is largely causal, that it’s possible to understand the causes, and that anything outside the easily-understood causal chain isn’t important.

The problem is that process-oriented thinking often fails when there isn’t a direct logical chain from A to B, but rather a probabilistic one.  Or stated another way, it can fail in cases where the multiple factors contribute to an outcome.  Often in such situations, people will focus on building a process-oriented logical chain that fits their biases and completely ignore factors that contradict those biases.

Gratuitous reference to sex

For instance, take abstinence-only sex education. Many people believe that teen sexual activity is a bad thing and most people believe that teenage pregnancies are even worse. So, based on these biases, it’s natural to promote an abstinence agenda. Even discussing birth control might weaken this abstinence message, so clearly abstinence-only sex education is a logical method for reducing teen pregnancy.  This process-oriented thinking seems completely plausible.

But if you actually look at the outcomes, you’ll see that abstinence-only education doesn’t actually work. In fact, it may actually increase teen pregnancy rates. One could speculate that this is because sex education isn’t the only factor that determines whether teenagers have sex. Perhaps, by discouraging communication about sex and not providing safe-sex information, teenagers are more likely to experiment, and do so in unsafe ways.

Or maybe there are other explanations.  But that doesn’t matter to the outcome-oriented analysis. Outcome-oriented reasoning’s role is to tell us that, even if we don’t understand the causes, abstinence-only education as practiced today does not reduce teen pregnancy rates. And that’s incredibly useful, because it strips out all the biases and just examines what works.

When it fails

Unfortunately outcome-oriented analysis also has one great flaw: post-hoc rationalization of decisions in a way that ignores basic tests of statistical significance. It’s easiest to understand this flaw using a simple gambling example.

Suppose I fly to Las Vegas and bet all my money on Red 14 in Roulette. And then suppose my number comes up and I win.  Woo hoo!  Every dollar I bet is now $35, not a bad return for 2 minutes of effort.  So does that mean that betting all my money on Red 14 was a great decision?

Of course not.  The odds of me winning were 1 in 37, and I only got paid 35 times my money.  It’s a terrible bet. On average, the house will take 5.5% of my money every time I do this. The fact that I happened to get lucky on one spin is irrelevant. In this case, the results of one spin are statistically insignificant.

Now, it’s easy to spot this outcome-oriented analysis flaw in Las Vegas, but it is far less obvious in other situations. For instance, suppose you are tipsy, but decide you’re sober enough to drive home safely. You do so, and make it home without hitting anything. Does that mean that you were actually sober enough to drive? No.  Driving drunk simply means that your chance of an accident is elevated, not that you will have an accident. Maybe you were so plastered you actually had a 50% chance of hitting someone, but when you flipped that coin, you were lucky enough to have it land on heads rather than tails.

The bottom line

Outcome-oriented thinking is powerful. It can eliminate bias and show what really works. But, it is only relevant when the outcomes are statistically significant–one can’t justify a decision simply by saying “it worked”.


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