Biased decision making
Let’s say you want to ban remote work at your office because it’s hurting the productivity of your organization. How much of your decision has been influenced by bias?
Could it be that you talked to Bob and Alice? You know they are stellar developers who like to work from the office. But both of them have complained that they feel hamstrung by absentee co-workers.
This is an example of Selection Bias, where your insights are skewed by a small population sample.
It could be worst. You could have asked Carol to look at company VPN logs to confirm that indeed the staff has not been checking in regularly.
That would be an example of Confirmation bias, favoring information that confirms our preconceptions.
I have yet to see a study that shows that VPN usage is directly tied to performance. You are using a metric you can easily come up with as a proxy for data that might be hard to get. How do you even measure productivity? Do you know?
Here are some common forms of cognitive bias that affect your decision making:
- Confirmation Bias: selecting information that supports existing beliefs
- Selection Bias: selecting non random sampling
- Survivorship Bias: sampling successful subjects only
- Availability Bias: relying on immediate recollection
The list of cognitive biases is long!
It all comes down to selecting the wrong data to make decisions.
So let’s say you want to ban remote work at your office because it’s hurting the productivity of your organization. Given all of the above, how should you proceed?
Well for one, don’t make a decision before you can justify it. If you have made a decision but you can’t fully justify it, assume that it is likely to be biased.
Try to come up with a rationale to justify the opposite stance. See how far you can argue against your initial decision.
Widen your views and gather more data points before making decisions.