Can you build a house without a floor plan? Or, cook without knowing what you are making. Similarly, you cannot develop stunning products without a design hypothesis to validate: the starting point of your user research. It is worth noting that you cannot influence your users to perform specific actions without understanding their pain points and objectives, which makes the hypothesis the cornerstone of the UX research and design process.
A design hypothesis serves as the guiding light for your UX research initiative, helping you gather accurate insights and determine whether your UX development is on the right track.
This article sheds light on the role of hypothesis in UX research and its importance in the overall design process.
Understanding the UX research hypothesis
Hypothesis in UX research is the process of validating a claim, assumption, or statement.
For example, your team believes that the conversion rate is low due to the button text’s placement and changing its location will increase the click-through rate (CTR) by 15%. Now, this is an assumption or a theory. However, although this is a good starting point for your research study, this assumption will only qualify as a hypothesis when the rationale behind the change is mentioned.
With this hypothesis, you can now run various UX studies, such as usability and A/B tests, to either accept or dismiss the assumption, guiding you to make informed product development decisions.
A hypothesis typically has three core components:
- Expected tangible outcome
- The reasoning or motivation behind this change, including research, historical data, KPI, web analytics, etc.
- The proposed design change your team things will give better results
Why must UX teams prioritize hypothesis?
“Each design is a proposed business solution — a hypothesis. Your goal is to validate the proposed solution as efficiently as possible by leveraging user input and feedback.” This quote by Jeff Gothelf, author of Lean UX, highlights why design teams must refrain from treating design as the final answer, but an assumption that needs to be validated with data collected through user feedback.
This is where hypothesis steps in, serving as a compass to guide you on the right path while researching a product or design change.
Some of the main reasons for starting your research with a hypothesis include:
- A well-structured hypothesis ensures that UX teams can prioritize research projects according to their business goals and existing data
- UX teams can justify the design decisions to all the relevant stakeholders across different teams, ensuring everyone understands the thinking behind every product-related change
Using hypothesis across different UX research methodologies
Hypothesis is one of the most crucial aspects of UX research. This section highlights the benefits of using hypothesis across different UX research methodologies.
Preference testing
What is it?
Preference testing is a testing technique that helps UX teams identify which feature, design version, layout, messaging style, navigation, etc, is more popular among users.
The hypothesis here could be: “Users will like vertical product images better than horizontal images since it is easier to scroll vertically.”
A hypothesis ensures that preference testing does not turn into a popularity contest, but a reliable way to make better and informed design decisions on targeted and specific elements.
Sample size: Target over 20 participants to ensure you have sufficient directional insights, guiding you to make data-driven design decisions.
Use UserQ’s Preference Testing tool to identify user preferences and, more importantly, determine the rationale behind their choice.
Card sorting
What goes where? Card sorting addresses these concerns, allowing UX teams to stay on top of how they bifurcate the different content and features. This testing methodology is crucial to designing and developing seamless navigation and information architectures.
Hypothesis: Users are likely to look for the best products under the ‘Trending Products’ tab instead of ‘Top Rated’.
In this case, you can identify whether your language resonates with your users, especially during product discovery, with this hypothesis.
Sample size: Opt for a sample size of more than 30 participants to be confident about the identified grouping patterns.
UserQ’s Card Sorting tool helps you dive deep into your users’ mental models and gain clarity on how users segment certain content and topics.
Tree testing
Can the user find what we want them to find without visual distractions? Tree testing helps product teams determine how easily users understand the navigation and labeling of the product’s information architecture (IA). In short, evaluate whether users can find the core content of the page or access certain features without any barriers.
Hypothesis in this case could be, “Users will look for how-to guides on the Learn page, rather than Resources.”
This allows teams to discover mismatches between the navigation structure and user expectations, enabling you to tweak categories and labels before diving deep into the design and development process.
Read this guide to learn how you can build a tree test with UserQ.
Prototype testing
Prototype testing is primarily used to determine how users interact with a product’s interactive prototype and identify the key areas of improvement and friction points to deliver better experiences.
The hypothesis here could be, “Users can fill the Product Demo form in two minutes since it contains very few mandatory fields”. It is used here to validate the claim and eliminate the guesswork about the overall flow of this particular activity and expected vs actual time taken to fill the Product Demo form.
Surveys
Surveys are predominantly used to uncover quantitative insights, including user attitudes, opinions, and values, to understand user perceptions about a topic, or to grasp the pulse of the audience to improve a product.
The hypothesis can be on the lines of, “Our users are confused about the pricing structure on the checkout page, resulting in cart abandonment.” You can validate this by creating a short survey, specifically designed for users who have abandoned their carts.
First click and 5-second test
Both the first click and the 5-second tests are short and rapid tests to evaluate what users see in the first five seconds or where users click first.
Here’s an example of a hypothesis relevant here. “Users will remember the brand’s logo and colors after interacting with the landing page for five seconds.” This way, product teams can validate whether their logos and brand colors are easily recognizable with a strong visual identity.
Creating a reliable design hypothesis
Before we proceed, please note that there isn’t a thumb rule or a pre-defined template for creating a design hypothesis. While the approach may vary depending on the goals of the UX research, there are a few unmissable elements of a well-defined design hypothesis.
These include the design change, objective, and assumptions. Let’s explore each element.
Objective
What is the purpose of validating your hypothesis? Product teams must clearly understand this and the impact these changes are expected to create. A well-defined objective ensures you aren’t just testing, but also validating your hypothesis to get closer to the desired outcome.
Assumptions
Now, to the most critical part of the exercise: why do you think the proposed design change will work and achieve the outcome you are looking for? Assumptions could be anything from pure guesses, insights gathered during user interviews, or reliance on historical data. Make a note of all the assumptions you are basing your hypothesis on.
Here’s a quick checklist you can follow while creating a design hypothesis.
- The changes should be testable
- The hypothesis must be specific
- The hypothesis should be linked to a tangible outcome
Design change
What are you changing, and what are you trying to do? The design change section should encompass all the intended changes to a particular feature, the layout, or other areas of the product. The idea is to understand what area of the design is expected to change and how it will enhance user experiences.
Do it right: common mistakes to avoid while creating and using a hypothesis.
Teams can jeopardize their UX research by not giving their hypothesis enough thought or misusing it. Here are some mistakes you should avoid for an accurate and unbiased research process.
Vague hypothesis
“The menu structure will improve the product experience ”. This is an unclear and vague hypothesis since it doesn’t state how you will track the product experience. A well-structured hypothesis should be specific, so the better way to put this would be. “We think modifying the menu structure by adding ‘New Launch’ will improve the product discovery of new products by 70% since it is more intuitive.
Confirmation bias
It is easy to stray away from what you believe, instead of uncovering the insights and patterns your data shows you. Additionally, do not misuse the data to push what you already believe to conclude.
Hypothesis validation
It is worth noting that, at times, your initial hypothesis could be wrong, so do not dismiss the unexpected outcomes. If the research results do not align with your hypothesis, go the extra mile to understand why the hypothesis failed.
Hypothesis to data-driven action
Do not get disheartened if your hypothesis is wrong. Use it as a learning opportunity since it still tells you a lot about what your target audience disapproves of and what they expect. These insights can help design and product teams to tweak their design strategy at the right time instead of wasting time and resources.
Always remember: even if the test does not align with your hypothesis, it provides clarity on how to proceed with product development.
Final thoughts
UX research is not black and white, and more often than not, uncovers the gray areas of user behavior and preferences. What may work for one user group may not work for the other user group, which makes hypothesis an important pillar of UX research. It also ensures that relevant stakeholders aren’t blindly trusting their personal biases, but using data to validate their assumptions.
A well-structured, specific, and measurable hypothesis paves the way for frictionless and smarter decision-making. The key is to start with a thought or question, validate it, and implement the design changes accordingly.
Build the next test in minutes using a clear hypothesis with UserQ today.
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