How To Eliminate Your Bias And Make Better Decisions

How To Eliminate Your Bias And Make Better Decisions

(pictured above: Ebbinghaus illusion; the two orange circles are exactly the same size)

If you are in any managerial role, your most important output is the decisions you make. Below you can learn 5 steps to reduce bias and make better decisions.


To eliminate bias we need to work by the numbers. In order to do so we have to work by the scientific method:

  • decide what problem you want to solve
  • develop a clear hypothesis
  • design and run an experiment to test the hypothesis
  • collect and analyze the data to get an objective answer

Our brains are hardwired to make decisions with as little effort as possible. It’s just biology, the brain uses a lot of energy and so we default to making decisions based on intuition. The problem is that most business decisions should not be made by gut feeling.

ask the right question

A decision is always the answer to a question. “Is advertising worth the cost?” or “Should we open up a new location?” Indeed, the first question that comes to mind is going to be too big. There’s too many variables that influence the answer, so we need to get more specific. “How much are Instagram ads increasing revenue?” is a better question because it already hints at the data we need to collect to answer it.

commit to a plan and stick to it

To eliminate the emotional bias it helps to commit to a plan upfront and then resist any temptations to change it.

You can see this simple dynamic in dieting. You have to know upfront what the plan is, or else, in the moment, you’re always going to find excuses to ‘cheat’. The same goes in business. If you can’t decide upfront on a plan and commit to it, you’re always going to find ways to adapt your plan based on your moment to moment emotional state.

Design the experiment upfront and commit to run it to the end. Don’t deviate from the plan unless there are critical reasons to do so.

collect the right data

Today most companies have an abundance of data, but you have to reach a good balance for both quality and quantity of data used.

Too little data and you won’t reach the right conclusion, too much data and you run higher costs for collecting and processing it. On the quality side you need to strive for good enough data. Poor quality of data is clearly not useful, but on the other end of the spectrum striving for perfection of the data could exponentially increase costs.

Ask yourself “what data do we need?”, “does anybody in the organization already have it?” and “how confident are we on the data?”

let the data speak

As you run your experiment, report on multiple outcomes. If the results are in line with your expectations, good; if not that’s also good. Remember that the goal of the experiment wasn’t to confirm your existing beliefs, but to put them to the test.

Once you have a batch of results ask yourself if you have found the answer to your question and you are confident about it. If the answer is ‘no’ to either then maybe you need to refine your experiment and run it again.

challenge your findings

Keep an open mind about the possibility that you’ve missed something. If you’ve made a decision based on the numbers and not personal bias, then you should be confident in having your peers challenge your findings.


  • make sure the question you ask is relevant to the problem you are looking to solve. And make the question specific
  • identify the underlying hypothesis in your question and design an experiment to test it
  • collect and analyze the data. Try to strike a balance in the quality and quantity of data used.
  • report on the numbers on multiple outcomes to make sure the results are consistent
  • ask your peers or use other methods to challenge your finds.

Follow the steps above on any decision of consequence. You probably don’t need to pull out a Spreadsheet to gather data and analyze it to make a decision on which shirt to wear today. For decisions that have a considerable impact in your business, however, it’s important to follow a rigorous decision making process to reduce bias.

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