Suppose a problem occurs in your department — how do you discover its cause? Do you think of the most likely cause, and gather evidence to back it up? Or do you step back from the problem and focus on patterns and feedback loops that lead to it?
All too often, instead of finding out what is happening, people assess what they think should be happening — an approach that can lead to confirmation bias.
Confirmation bias happens when you look for information that validates your beliefs. It’s easier to confirm an idea than challenge it — but the danger is that you could miss critical information.
To help you avoid the tendency of confirmation bias, use hypothesis testing. In this method, you begin by asking a “what if” question, followed by a series of “if … then” hypotheses. It works like this. A marketing manager asks “What if increasing a campaign budget improves sales?”
She follows this with “If there’s a bigger budget, then we can afford more advertising, resulting in more sales.” She then states “If increasing the budget has no effect on sales, then we’ll have made a net loss.”
She then evaluates any data available to her to find out if her decision will have a positive or negative effect on the problem or objective at hand. This technique allows you to come up with a variety of hypotheses without sacrificing the ability to explore new ideas and approaches.
Besides hypothesis testing, you could also try using an influence diagram to understand causes and effects. Let’s say you’re a finance officer and you’re assessing the pros and cons of a decision to increase employee salaries. You want to understand how different variables affect this decision.
In an influence diagram, the issue you’re considering is shown in a rectangle, and variables are shown in ovals. All the elements are linked by directional arrows, which indicate their direction of influence. Plus or minus signs indicate an increase or a decrease in the value of the influence.
So in this example, increasing basic salaries may increase costs, but might improve morale. And in a positive feedback loop, increased morale could lead to lower staff turnover, further increasing morale.
Lower staff turnover also has positive effect on customer relationships because sales staff will have more time to develop customer relationships. It could also decrease costs because you don’t have to invest as much into training new staff.
Finally, improved customer relations can result in increased profits, which in turn might negate the rise in cost due to the salary increases. This diagram clarifies that the savings associated with not having to train new employees — and the increased profits from improved customer relations — will mitigate the costs of salary increases.
Causality is more complex than just linear cause and effect. Use hypothesis testing and influence diagrams to help you understand the complex relationships among variables.