Many people are skeptical of science these days. Though my successes over 30 years have been based on scientific learning, I understand why. Scientists make mistakes like everyone else, but the profession makes it easier to admit that, so the public sees the errors. Some cross ethical lines, as in every profession. Most scientists have a hard time explaining themselves to the average reader, the same way you have trouble explaining what you do to nonexperts. The media often report the latest scientific studies without the larger context, so it seems like scientific consensus is changing when really, there was no consensus yet.

I could write several posts detailing all the reasons. But instead I will argue that managers would nonetheless be more successful if they made decisions like scientists. Here’s why: Actions you take as a manager often work, or fail, for reasons you don’t notice. By accounting for those reasons, you won’t give up on a good idea that failed but could work in different circumstances, or try to apply one that succeeded where it won’t.

All of us, myself included, tend to think when we make decision “A” and then a nice result “B” happens, our action A caused B. Yet if B was bad, most of us look for other factors to blame! In either case, here are some possibilities:

• B might have caused A. This can happen when B was already under way, but no one noticed before A was started. An example might be when you decide to modify performance appraisals, and surveys find later that trust between management and employees went up. But maybe you unknowingly felt comfortable making that decision because mutual trust was already going up. Higher trust caused the appraisal decision, not the other way around.
• A and B could be causing each other. This means a little A happens, which causes a little B, which allows more A, which allows more B, and so on. The previous example serves here if, say, a pilot test of running a division without appraisals raises trust there further, which causes wider implementation, which raises trust more, which results in enterprise-wide changes and higher trust.
• Factor “C” has to be in play, or A doesn’t cause B. Some scientists call these “mediating” variables, and these often explain why earlier studies found different results while looking at similar things. Factor C might only “moderate” the relationship, too, meaning A does cause B, but B would be higher or lower without C.
• Factor “D” caused both A and B. For example, you might think your decision (A) not to backfill a project manager position raised people’s sense of empowerment (B) in worker surveys. Instead, the earlier introduction of Agile (D) by your bosses made it possible for you to do without a PM, and also increased empowerment. Decision A may have had no effect, or it may be a moderating variable. (In this example, it likely would enhance empowerment further.)
• A only causes B in situation “E.” For instance, you apply Kanban to one team and everybody loves the results. But with another team it fails, even though you did everything exactly the same way! That’s because Kanban was appropriate for the first team’s deliverables, but not the second team’s.
• A only causes B in environment “F.” You easily implement Agile in one company. Then you change jobs to a direct competitor—with similar workers, technology, and customers—and Agile adoption fails. Maybe the first company communicated openly and everyone knew it was in trouble, but the second company withheld the bad news from workers, so the motivation to change was missing.

Making all of this worse is that almost any combination of these scenarios could be in play at the same time. And again, as this graphic illustrates, that is true whether the result was good or bad:

If you don’t consider all of the variables in any case, you can’t be sure whether your own decisions were good ones, much less those of other managers, bloggers and presenters giving us advice. One problem with success stories in business magazines is they often get condensed into a few Factor A points of advice that may not apply to your factors C through F.

Thinking like a scientist to make big decisions isn’t all that hard. It just takes a little time and discipline:

1. Make your question or problem-statement as specific as possible, keeping it to one sentence—scientific studies usually have a short list of single-sentence hypotheses.
2. Check for scientific studies, books by Ph.Ds., and then other Web opinions on the answer/solution—the bigger the decision, the more sources you should review, but give more weight to good evidence.
3. In each case, notice:
   • Other factors that could have caused the results claimed (good or bad),
   • Differences between the organizations discussed and yours, and
   • Differences in the situations described.
4. Share what you found with everyone in your organization affected by the decision.
5. Go with their consensus.

Not only will this approach give you the most effective decision most of the time by capturing the most variables, it will provide participation in decision-making proven to raise worker satisfaction, and reduce resistance to the approach you choose. True, you will have to share the credit for the good decisions, but you’ll still get plenty because you led the process. Plus, you don’t have to take all the blame for the bad ones!

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