Design Hypothesis.

This short insight is from of our Methods series: A discussion of useful working practices and ideas for better ideation and execution.  We discuss what each method is, why they are useful, and how to utilize them.


Framing work as a hypothesis encourages our thought process to extend beyond just the outcome of a product or service. It forces us to carefully examine whether the processes selected will serve to help us achieve the intended goals and outcomes.


Having a hypothesis streamlines the team's understanding of an issue. Its concise nature helps keep everyone on the same page, focused on the same goal. Another advantage is it leaves room for improvement. If one approach doesn't result in an expected outcome, the natural response is to come up with an alternative hypothesis.  

How To Do It.

  1. Gather your team and ensure that everyone is aligned on the problem that you are all trying to solve.
  2. Collectively write out the hypothesis that addresses the problem(s) you are trying to solve. It's helpful to begin by forming broad hypotheses and streamline them to become more specific later. An example of a structure is as follows: We believe that designing/creating [a] for [b users] will result in [c outcomes]. Our hypothesis will be confirmed once we achieve [x metric/signal].
  3. Once a hypothesis is formed, it's critical to outline a list of 'limitations' in order for the team to be prepared against unexpected outcomes and biases. For example: This doesn't take into consideration [d users] if [c outcome] happen.
  4. Determine a user touchpoint that will allow you to test your hypothesis, for example social media, the website, a call-to-action button. Test your hypothesis. If an unexpected outcome is achieved, examine the outcome and refine the hypothesis accordingly. Repeat until you reach your goals.

Further Discussion.

The Design Hypothesis method builds logically on top of the Five Whys method—essentially asking 'why' five times (or more) in order to dig to the root cause.

Before selecting a solution, and analyzing our chosen method, it makes logical sense to truly understand the problem you're trying to solve. Through formulating a hypothesis, we develop a framework that allows for testing and iteration that result in hard data to support the rationale of our executions. While a hypothesis may not always match up with the results, the intelligent estimations help guide inspired decision making and reduce potential error.