Prototyping Made Smarter: Start With the Right Hypothesis

Every great idea starts with a question: What if this could work? But taking the leap from idea to reality requires more than just building something—it requires knowing what you’re trying to validate. Without clear hypotheses guiding your efforts, even the most well-designed prototype or pretotype can lead to wasted time, energy, and resources.

Hypothesis testing is the backbone of successful prototyping. It ensures that your experiments are purposeful, your assumptions are grounded in reality, and your solutions are built on a foundation of evidence—not guesswork.

This article will guide you through designing impactful hypothesis tests and crafting meaningful questions to turn uncertainty into actionable insights.

What Is a Hypothesis, and Why Does Having One Matter for Prototyping?

A hypothesis is a specific assumption about your solution that you need to prove or disprove—and it’s the foundation of every effective experiment in prototyping (and pretotyping). Whether you’re building a product, designing a service, or pitching a bold new concept, your hypotheses about how it will work, who will want it, and whether it can succeed drive your efforts.

These assumptions keep you focused on what really matters and help you avoid costly guesswork. They help answer key questions like:

• Does this solve a real problem for your audience? (Desirability)
• Can it be built and work as intended? (Feasibility)
• Will it support a scalable, profitable business model? (Viability)

By testing these hypotheses, you’re not just validating an idea—you’re unlocking insights, refining your solution, and building confidence that your idea is on the right track. It’s how big ideas move from assumptions to action.

Designing Your Hypothesis Tests

In the early stages of prototyping, focus on discovery: observing behaviours, motivations, and areas for improvement. Later tests should prioritise validation: ensuring your solution aligns with user expectations and functions as intended.

With that said, make the most of this process by carefully considering who you’re testing with, what you’re testing, and how to design experiments that deliver impactful results.

Picking the Right Audience to Test With

The success of your hypothesis test depends largely on who you include in the process. A well-chosen audience doesn’t just provide feedback—they challenge assumptions, validate ideas, and uncover opportunities you might not have considered.

Different groups bring unique perspectives, making it essential to balance between those familiar with your solution and those encountering it for the first time. Here’s a breakdown of potential test audiences and the value they bring:

1. Familiar Faces
   Start with the people you interviewed during customer discovery. Their familiarity with the problem and your solution makes them ideal for validation, as they can assess whether your idea aligns with their needs.
   
   
2. Target Users
   Engage directly with your specific audience or niche use case. Their feedback is crucial for proving or disproving hypotheses tied to the prototype’s intended purpose.
   
   
3. New Testers
   Involve fresh eyes to gain unbiased feedback. New testers can highlight what’s confusing or exciting, offering inspiration and insights into user engagement.
   
   
4. Extreme Users
   Test with those who use similar products intensively or avoid them entirely. Their unique perspectives can reveal innovative use cases or emerging trends.
   
   
5. Other Stakeholders
   Include industry experts, salespeople, customer service reps, or regulators. These stakeholders can provide critical insights into feasibility, usability, and compliance challenges.
   
   

Regardless of the audience, keep your testing practical and focused. Opt for individuals who are easily accessible, and try to conduct your tests in real-world environments where your solution is most likely to be used. Testing in a realistic context ensures the feedback you receive is relevant and actionable, helping you refine your solution.

What Should You Test?

The nature of your test depends on the assumptions you want to validate. Are you exploring desirability (whether users want your solution), feasibility (whether it works for them), or viability (whether it’s sustainable as a business)? Formulate hypotheses that are specific, testable, and tied to these areas to structure your tests effectively.

Desirability

When it comes to desirability, interviews or surveys are a great way to understand user preferences. Questions you want to consider for desirability can be as simple as:

• Does the tester like the solution you are showing them?
• Would they recommend it to their friends?
• Which other product would they compare this with? Which one as better than what they see in front of them, and which one as worse?
• Compared to other solution, what is something that stands out here?
• Would they be willing to pay the price you are offering this for?
• Who else do they think would like this product? And why?

Feasibility 

Much can be learned from observing users interacting with your prototype in real-world conditions. However, some questions around feasibility could be: 

• Do they understand instantly what the solution is and what its purpose/function is?
• What do they not understand?
• Can they summarise how this product works?
• How would the tester use the solution you are showing?
• When and where would they use it?
• What would they use the solution for? To achieve what?

Viability

Viability testing is essential for determining whether your solution can support a sustainable business model. To do this effectively, create scenarios where you can observe and track how test subjects engage with your idea in a real-world context.

One of the most effective tools for viability testing is Alberto Savoia’s XYZ Hypothesis Framework, which helps you define clear, measurable assumptions:

• X: A specific percentage of your target market.
• Y: A clear description of your target market.
• Z: The behaviour you expect from your market.

Using this framework, you can translate assumptions into actionable tests with the formula:
X% of Y will do Z.

For example:
"At least 25% of diabetes patients visiting the local clinic will join the waitlist for an $80 per month meal box subscription."

The Importance of Defining Your X

Defining X, the percentage of your target market, is critical because it ties your solution’s viability to your business goals. While defining Y (the market) and Z (the behaviour) often stems naturally from your problem statement, determining X requires balancing ambition with realism.

Ask yourself:

• What percentage of the market do I need to capture to achieve profitability?
• What percentage is realistic to aim for in the short term?

For example, targeting 0.1% of the diabetes market might be easier to validate, but such a small share may not justify the investment needed to scale the business. Conversely, aiming for 10% might make your idea attractive to investors but requires more rigorous testing to confirm its feasibility.

Which Hypothesis Should You Test First?

After identifying what to test—whether it’s desirability, feasibility, or viability—the next challenge is deciding where to begin. With numerous assumptions about your solution—ranging from its design to pricing and distribution channels—it’s easy to feel overwhelmed. However, prioritising the right hypothesis ensures you focus resources effectively and build momentum with actionable insights. So where do we begin?

Start With the Most Crucial Assumption

Here’s a more fluid and engaging version of the section:  

The best way to start testing is by focusing on the hypothesis most critical to your solution’s success, while keeping resource use to a minimum. In many cases, this means starting with desirability—whether users want your solution and are willing to pay for it. Desirability is not only fundamental to your success, but is also one of the easiest aspects to test early in the process.  

Testing feasibility—whether your solution can be built—before confirming desirability can be a costly mistake. After all, there’s no value in investing time and resources into a solution that no one wants. That’s why validating user interest should generally take precedence.  

That said, there are exceptions. If your solution is already proven desirable and viable, feasibility may need to take the spotlight. For example, complex technologies or hardware solutions might require technical validation earlier to ensure they can function as intended.  

Leap-of-Faith Assumptions

Eric Ries, in The Lean Startup, describes the “leap-of-faith assumption” as the riskiest element of your solution—the assumption that must be true for your idea to succeed. Identifying and testing this assumption early is vital.

For instance, Netflix’s leap-of-faith assumption was that people would have fast and reliable internet connections to stream movies. Without this, their entire business model would have been unworkable. For your solution, ask yourself: What is the riskiest assumption we’re making about our solution? What absolutely needs to be true for this idea to work?

Critical Function and Critical Experience Prototypes

Another approach to prioritisation is to focus on your solution’s critical function or critical experience.

• Critical Function Prototype:
  This identifies and demonstrates the essential functional elements your solution must deliver to meet user expectations and compete in the market. For instance, at ProGlove, speed and accuracy were paramount for customers conducting thousands of scans daily. The prototype had to achieve 90%+ accuracy to be taken seriously in the scanner market.
• Critical Experience Prototype:
  This emphasises the emotional or experiential elements of your solution that resonate with users. For ProGlove, the concept of a scanner integrated into a glove provided a unique form factor, motion control for scanning, and a “cool” factor that drove adoption.

Which Hypothesis Should You Test First?

When working with prototypes, deciding which hypothesis to test first can feel overwhelming. Prototypes are powerful tools for proving or disproving the assumptions you have about your solution—but with numerous possibilities, prioritising is essential to avoid wasting time and resources.

The general rule is to begin with the hypothesis most critical to your solution’s success while using the least amount of resources. For many startups, this often means starting with desirability: understanding whether users want your solution and are willing to pay for it. Testing desirability is typically straightforward and provides crucial insights into whether your solution has market potential.

On the other hand, testing feasibility—whether your solution can be built—before confirming desirability is risky. Investing time and effort into validating whether something works is pointless if no one wants it. However, there are exceptions. For example, complex technologies or hardware-based solutions might require testing feasibility first to ensure the solution is even possible.

Leap-of-Faith Assumptions

Eric Ries, in The Lean Startup, describes the “leap-of-faith assumption” as the riskiest element of your solution—the assumption that must be true for your idea to succeed. Identifying and testing this assumption early is vital.

For instance, Netflix’s leap-of-faith assumption was that people would have fast and reliable internet connections to stream movies. Without this, their entire business model would have been unworkable. For your solution, ask yourself: What absolutely needs to be true for this to work?

Critical Function and Critical Experience

Another way to prioritise is by focusing on the critical function or critical experience of your solution.

• Critical Function Prototype: This addresses the core feature of your solution that directly solves the user’s problem. Without delivering on this need, your solution won’t be taken seriously. For example, a barcode scanner’s critical function is speed and accuracy—without these, the product would fail to meet market expectations.
• Critical Experience Prototype: This centres on the emotional or experiential value of your solution, often tied to your vision. It’s what makes your product stand out from competitors and excites users. For example, a wearable fitness device might differentiate itself through sleek, intuitive design and a seamless user interface that inspires loyalty and engagement.

Testing these elements ensures that your solution is not only functional but also uniquely appealing to your audience.

Uncovering Unknown Unknowns

While testing known assumptions is important, one of the greatest risks lies in unknown unknowns—the assumptions you aren’t even aware you’re making. These hidden factors can undermine your solution if left undiscovered.

To uncover these risks, consider the following strategies:

1. Continue Interviews: Even during the prototyping phase, ongoing conversations with potential users can reveal unexpected insights and highlight overlooked needs.
2. Engage Industry Experts: Experts in your field can shed light on important metrics, market challenges, or technical hurdles you might not have considered.
3. Prototyping for Discovery: Building and testing prototypes often exposes unforeseen issues, whether technical limitations or user experience challenges.
4. Benchmark Competitors: Analyse existing products or services that address the same user needs. Break them down to understand how they work, what technologies they use, and how they create value. This isn’t about copying but identifying gaps and opportunities for improvement.

For example, by dissecting competitor offerings, you might discover an element of their solution that inspires an innovative feature for your product—or highlights a pain point they failed to address.

That’s a Wrap

Hypothesis testing is the foundation of successful prototyping and innovation. By focusing on your riskiest and most critical assumptions, you can validate your ideas, uncover unknown risks, and refine your solution. Each test is a step toward building something that users want, works effectively, and thrives in the market.

Remember, testing is an iterative process—every result brings new opportunities for growth and improvement. With thoughtful planning and a clear focus, you can turn uncertainty into informed decisions that drive meaningful progress.

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