What Are You Missing?

Many errors are errors of perception, not logic.

How we look at the world affects the opportunities we pursue and the risks we mitigate. Over time, mindset and perspective shapes reality.

But we’re all susceptible to a range of biases.

We tend to find data that confirms what we already believe. We place too much weight on the status quo and avoid considering novel situations. We overestimate our influence. We seek more data, even when it’s not helpful. Our thinking is framed by the way information is presented. And we can be blind to useful data that is missing.

These are just a few of the many biases that lead to flawed decisions.

Focus on where the holes ain’t

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(Source: Wikimedia Commons)

A classic example is a story about plane improvement in World War II. As bombers returned from battle, researchers analyzed the bullet holes in extensive detail. The military then reinforced the parts of the planes most often hit by bullets.

That is until one bright spark pointed out they were reinforcing the wrong parts of the plane. They had been looking at places where a plane could get hit and still return safe to base. Instead, they should reinforce the other parts, where a bullet stops you making it back!

This is a classic situation of information bias and survival bias. We have plenty of available data, so we jump straight into analyzing it. But it’s actually the data we don’t have that matters. That is, the data about where bullet holes cause the planes to crash.

Avoid the narrow frame

Here’s another example. Various initiatives exist to help more women progress to senior roles in organizations. For example, more attractive maternity leave, childcare subsidies, and gender specific quotas.

But a recent study found a surprising fact. What was the number one initiative linked to a higher percentage of women in senior roles? Paternity leave. That’s right, an initiative not aimed at women at all.

This is a good example of framing bias. If we see female advancement as a women’s issue, we focus our solutions specifically on women. But the narrow framing of the problem prevents us from seeing other effective solutions. It turns out that when men get more involved in family life, then women get more opportunities at work. Plus, they also get the support at home needed to pursue senior opportunities.

Don’t just listen to the usual suspects

One last example. Think of the last time you did some customer research for your organization. How did you source your participants? 

Chances are you used email, website notifications, in-store promotion and/or social posts. All these methods are convenient, easy to use, and very low cost (if not free). You may even have achieved a reassuringly large sample size.

But stop and think: did you hear from all the people you need to?

Did you go out of your way to survey people who are not currently your customers? What about inactive or occasional customers? What about those who haven’t even heard of your brand?

If not, you may have skewed the survey responses to those who are already most engaged with your brand. Hello sampling bias. And most likely also expectation and confirmation bias.

Your survey results could be like the bullet holes in WWII planes: obvious, available, and ready to analyze. But they may also be dangerously misleading.

Ask yourself: what am I missing?

So before you make a key decision or crunch some important data, stop and pause for a moment. Ask yourself: am I missing something?

Do I have the full picture? Have I got input from outside the usual suspects? Have I sought dissenting views or had people challenge my assumptions? Am I using too narrow a frame to define the problem?

Chances are you’re missing something important…

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