How to design assumption-killing interview questions

But there is a high chance you learned absolutely nothing.

In the world of product design, validation feels like the goal. It feels like success. But if we are being honest with ourselves, validation is often the most expensive drug in the startup world. If your interviews make you feel warm and fuzzy, your product might actually be in danger.

Most interviews do not fail because we found the wrong users. They fail because we asked the wrong questions. We unintentionally design questions that protect our egos rather than challenging our ideas.

If you want to move from being a designer who executes tasks to a strategic partner who guides product direction, you need to change how you research. You need to stop collecting compliments and start hunting for the truth, even when it hurts.

Here is how to turn vague product ideas into assumption-killing interview questions that actually reduce business risk.

The real enemy is comfort

Let’s be real about what happens in many product teams. We have a roadmap. We have a deadline. We have a founder or a product manager who is in love with a specific solution. In this environment, research often becomes a box-checking exercise. We are not there to learn. We are there to get emotional reassurance that we are building the right thing.

This is what I call "risk destruction" versus "insight collection."

When we prioritize feeling good, we fall into validation addiction. We ask questions that invite users to lie to us politely. We show them a prototype and ask if they like it. Of course they like it. They are nice people, and you are sitting right there.

But "liking" a product does not mean they will pay for it. It does not mean they will change their daily workflow to use it.

Desirability, viability, and feasibility are not just buzzwords for your portfolio. They are survival metrics for the business. If you conduct research that only confirms what you already believe, you end up with a team that is convinced they are right, a lot of wasted money, and a product that launches to silence.

You did not do research. You did therapy for your own idea.

Questions are the end, not the beginning

The biggest mistake designers make is starting with the interview script. It seems logical to sit down and write a list of questions. But questions are the final step in the process.

If you start with questions, you will likely ask things like "Do you think productivity is important?" or "Would you use a dashboard that uses AI?"

These questions are trash. They are hypothetical and lead the witness.

To get to the truth, you need to work backward using this structure:
Belief > Assumption > Hypothesis > Question.

Let’s say your team believes that "Users want an AI productivity dashboard." That is a belief. It sounds reasonable. It sounds like something people would want. But it is completely untestable.

To fix this, we need to turn it into an assumption. We need to apply the "reality test." If this belief is true, what must also be true in reality?

For users to want an AI dashboard, they must currently struggle with managing data across multiple tools. They must feel enough pain to switch. They must trust AI with their data.

Now we have something we can actually look for. We are no longer asking if they "want" something. We are looking for evidence that the problem exists.

Hypotheses are where rigor begins

Opinions feel smart, but hypotheses are where the real work happens. A strong hypothesis is dangerous because it can be proven wrong. If your hypothesis cannot embarrass you, it is too safe.

To build a strong hypothesis, you need three components:

1. Who (The specific actor)
2. Situation (The context or trigger)
3. Behavior (The observable action)

Let’s look at a weak example: "I believe managers want better reporting tools." This is weak because it is just an opinion.

Now let’s look at a strong example: "I believe startup founders (Who) managing multiple tools (Situation) will manually stitch together data in a spreadsheet at least once a day to understand their business (Behavior)."

See the difference? The second one describes a reality we can observe. It gives us a behavior to hunt for. If we talk to five founders and none of them are manually stitching data together, our hypothesis is wrong. And that is a good thing. It saves us from building a tool nobody needs.

Behavior always beats opinion. Always.

Designing evidence before designing questions

Before you write a single question mark, you need to decide what evidence looks like. You are not designing a conversation. You are designing a court case.

Most teams collect opinions because evidence threatens their roadmap. Opinions are safe. You can interpret them however you want. Evidence is binary. It either exists or it doesn't.

There are four types of evidence you should look for:

1. Past behavior: What have they actually done in the last month?
2. Current workflow: How do they solve this problem right now?
3. Emotional trigger: What specific moment causes frustration?
4. Decision criteria: How did they choose their current solution?

If you are building that AI dashboard, evidence isn't a user saying "That sounds cool." Evidence is a user showing you a messy, complex Excel sheet they update every morning at 8 AM.

Direct vs. indirect questions

Now we can finally talk about the questions themselves. There is a "trust line" in user interviews.

Direct questions feel good. They are straightforward. "Is security important to you?" But they are useless. No one is going to say "No, I hate security. I want my data to be stolen."

Direct questions invite users to perform their "best self." They answer as the person they wish they were, not the person they actually are.

Indirect questions reveal the truth. Instead of asking if security is important, you ask: "Tell me about the last time you evaluated a new software tool. What was the specific step that made you say yes or no?"

If they mention security audits, compliance checks, and data encryption, then security is important. If they only talk about price and speed, then security is a secondary concern, no matter what they claimed earlier.

Rule of thumb: If a question can be answered with "Yes" or "No," it is probably a waste of time.

The translation framework

This is where you turn business risk into human conversation. This is the method that separates the strategists from the ticket-movers.

You take your hypothesis and translate it into an indirect question that hunts for evidence.

Assumption: Users want a fitness app to track their calories.
Hypothesis: People who care about fitness track their food intake daily.
Bad Question: "Would you use an app to track calories?"
Good Indirect Question: "Walk me through exactly what you ate yesterday. Did you write it down anywhere?"

If they struggle to remember what they ate and didn't write it down, they are not going to use your app. It doesn't matter how pretty the UI is.

Assumption: SaaS teams need a better way to hire.
Hypothesis: Hiring managers are spending too much time screening bad candidates.
Bad Question: "Is hiring time-consuming for you?"
Good Indirect Question: "Open up your calendar for last week. How many hours did you spend on candidate interviews, and how many of those candidates were qualified?"

The answers to these indirect questions are brutal. They cut through the fluff and show you the reality of the user's life.

Contamination: How we poison our own research

Even with good intentions, it is easy to accidentally teach users to lie to us. We call this contamination. It usually happens in five ways:

1. Leading language: "How difficult is it to export data?" (You are assuming it is difficult).
2. Implied solution: "Would an AI bot help you?" (You are selling the solution).
3. Hypotheticals: "Imagine you had..." (Imagination is not data).
4. Future promises: "Will you use this when we launch?" (Future prediction is impossible).
5. Founder energy bias: If you sound excited, the user will mirror your excitement to be polite.

We need to scrub our questions of these toxins.

Instead of asking "Would AI automation help you manage your work better?", try asking "How do you currently manage your workload across tools?"

When you remove the solution from the question, you make space for the truth. You might find out they don't manage their workload at all. Or that they use a sticky note system they love.

Patterns or garbage

You finished your interviews. Now what?

One interview proves nothing. You cannot build a strategy on a single conversation. Patterns are the only currency that matters in business.

You are looking for repetition.

• Repeated pain.
• Repeated workarounds.
• Repeated delays.
• Repeated resistance.

If you interview five people and everyone has a different problem, you do not have a product opportunity. You have a consulting opportunity.

If patterns do not emerge, your idea is probably weak. This is a hard pill to swallow. It is tempting to blame the research, or the recruiting, or the script. But if the pain isn't consistent, the solution won't scale.

Recognizing the absence of a pattern is a strategic skill. It saves your company money.

The uncomfortable conclusion

If your research feels safe, you are probably lying to yourself.

Good research creates unease. It challenges your worldview. It forces you to ask hard questions about whether you are building the right thing.

Compliments are not data. Excitement is often a warning sign that you pitched rather than listened.

As you move forward in your career, you have a choice. You can be the designer who creates beautiful mockups for products that fail. Or you can be the designer who helps the business survive reality.

The next time you sit down to write a script, pause. Don't ask "What should I ask?" Ask "What belief am I trying to kill?"