Building high-quality hypotheses for better design decisions

The scientific method in UX Design.

Giorgio Schirò
UX Collective
Published in
5 min readMay 30, 2019

Is design an art or a science?

A lot of professionals still perceive UX Design more as an art than a science because they are not aware of the scientific processes behind the role.

The purpose of using a scientific method is to make sure we, as UX Designers, don’t base our decision on intuition or guesswork.

What is Data-driven UX?

“The new design hasn’t improved the conversion rate, according to the data.

The first thing to do would be to analyse the data and figure out what actions take in order to improve, however that is not always the case.

When a designer (or a product person) acts out of emotions, the outcome can be to throw the design away and complain about how much of a waste of time it was. Although common, that is not the analytical mindset we look for.

Before bin everything, we must carry several tests and use the aforementioned “data” as the basis for developing new hypotheses on how we can improve our product.

UX Decisions should not be made by discussions and opinions.

It’s clear that when we talk about UX decisions we need to leave opinions and emotions out of the table.

In order to make informed UX design decision, we need to understand how to formulate strong hypotheses and run experiments based on those.

Developing a Hypothesis

When stakes are high, decisions have consequences and making the right design decisions matters.

As designers, we are responsible to do our best to avoid bad decisions.

The right design decision can help to create the most user-friendly experience, but (even small) bad decisions can lead to disastrous consequences. Although most of the issues can be identified by measuring the consequences of a product after it is launched, that’s not enough.

Try to imagine a poorly designed interface for an aeroplane control panel. A wrong decision can lead to more chance of human error and potential loss of lives.

How can we prevent bad decisions?

Designers are not prophets. We don’t act based on revelations or inspiration. We can’t afford to bet everything on pure luck.

Designers don’t have premonitory dreams about what colour will boost sales. These decisions are the result of a systematic and iterative process of data collection, testings and experiments and the evaluation of the results. That’s the big difference between Prophecy and Prediction.

Building high-quality hypothesis based on predictions

First of all, designers must avoid the design-prophecy approach by introducing a system where design decisions are evaluated based on evidence.

Working in this way simplifies the decision-making process, because new features can be designed and built quickly and very consistently — and it’s also a scientific way of iterating and testing the effectiveness of design decisions in different situations.

Although the best designers have good intuition, human thought patterns cannot always be fully trusted.

That’s great, I know now how (sorry) important hypotheses are. But how can I apply this to my everyday life?

From hypothesis to prediction.

  1. Turn doubts into answerable questions

“How can we get more subscribers?”

Becomes

“If we changed the colour of the subscribe button to red, does that result in more click?”

2. Retrieve information in a systematic fashion

“A/B testing tells us that red is more effective than green”

“Red is considered as an alert/danger colour”

3. Data Validation

“Evidence has shown that users tend to click on the red button unless they have recently associated the red colour to something negative ”

4. Predictions are tested and adjusted based on performance

“Changing the colour to red will result in more clicks” 🧙

What should I ignore?

“It’s always been like this!”

“I don’t think this will be useful.”

“This is amazing, let’s do the same.”

Opinion, belief, intuition, rhetoric, guesswork, trends, advice, anecdotes, common practice and convention are all the opposite of evidence.

The most powerful tools for validating hypotheses are scientific research, user research, interviews and testing and existing guidelines and specifications.

How to implement a process based on hypotheses?

Remember: making informed design decision is not about taking sides, you should leave your personal taste and preferences at the door.

The point is to weigh the pros and cons of any implication before taking a decision and to validate your hypotheses against your data.

Define a systematic process to accumulate and evaluate data.

The collected information needs to be documented and assessed, including how it has been collected.

Hypotheses based on high-quality evidence lead to better design decisions.

Better design decision, based on both qualitative and quantitative data, can reduce or prevent negative consequence.

Useful tips:

  • Choose the method that best suits you to grade your hypothesis. Not all of them have the same level of quality.
  • Validate your hypothesis early in the process.
  • Prioritise decisions backed by high-quality data.
  • Put extra care on important functionalities and frequent interactions
  • Documentation is important! Everything should be documented: methods, processes, outcomes, etc. (Even when a decision is based only on a gut feeling.)
  • One is always better than zero. But keep in mind that a small research sample might skew your hypothesis.

References

“Evidence Based Design For Digital Products” Creative Navy https://medium.com/uxjournal/evidence-based-design-for-digital-apps-9720b6590f6f

“The Scientific Method and Data Science in UX Design” Asad Ali Junaid https://medium.com/nyc-design/the-scientific-method-and-data-science-in-ux-design-70a60fd1a89e

“How Can Data Science Improve UX Design?” Sophia Brooke https://uxplanet.org/how-can-data-science-improve-ux-design-3a3d123e5a9c

“What is Data-driven UX?” Capturly https://capturly.com/blog/what-is-data-driven-ux/

“Data Scientists Are The Next UX Designers” Laura Denham https://channels.theinnovationenterprise.com/articles/data-scientists-are-the-next-ux-designers

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