You’re in the business of understanding people. Not just what they say but what’s going on under the surface. And one of the most fascinating ways to get there? Misperceptions. Looking at the difference between what people think they know and what they actually know.
It’s an idea that’s been gaining traction in the research world. Not because it’s trendy or because it goes against the flow of instant-access to all the information in the world that AI has encouraged, but because it opens the door to more nuanced insight. The kind that helps you understand not just sentiment, but how beliefs are formed, where behaviors stem from, and what might shift them.
What are misperceptions in research?
Let’s start with a distinction: Attitudes, beliefs, and knowledge are not interchangeable. Yet in many surveys, we treat them as if they are. We ask how strongly someone agrees with a statement or how much they trust a brand. But what factual knowledge are they working with?
If you want to truly understand a respondent’s mindset, you need to know whether their beliefs are anchored in reality or floating freely in assumption. And that’s where things get interesting.
Because it turns out, people often feel extremely confident about things they’re completely wrong about.
Why misperceptions matter more than ever
In a landscape flooded with misinformation, social echo chambers, and algorithm-driven content bubbles, what people believe is increasingly shaped by exposure, not expertise. The Ipsos Perils of Perception studies repeatedly show that people consistently misjudge everything from demographics and crime rates to obesity levels and wealth distribution.
These gaps are insight-rich indicators of how people filter the world.
For researchers, misperceptions represent:
A window into cultural narratives.
A lens on behavioral intent.
A diagnostic tool for understanding resistance, bias, or decision-making.
Asking someone’s opinion without first understanding what they believe to be true can be misleading. If a respondent strongly agrees with a policy but misunderstands its core facts, then the data tells a partial story at best.
From measurement to meaning: What this looks like in practice
Let’s say you’re researching attitudes toward electric vehicles (EVs). You ask how favorable someone feels about EV adoption. Great. But what if that person overestimates the percentage of EVs on the road by 300%? Their response suddenly carries a different context, doesn’t it?
Understanding misperceptions doesn’t invalidate attitude data; it adds dimension to it. That’s the key. You’re not undermining opinions; you’re enriching your interpretation of them.
And there’s more. Misperceptions can tell you:
Where messaging may be landing off-target.
What level of public understanding your client needs to work with.
How to segment audiences not just by demographics or preferences, but by knowledge baselines.
Confidence ≠ accuracy: A subtle danger in surveys
Here’s one of the most quietly problematic dynamics in research: High confidence in low knowledge. It’s the Dunning-Kruger effect in action. Respondents with the least accurate information often feel most sure of themselves, which can skew interpretation if we’re not careful.
By incorporating questions that measure factual accuracy (without encouraging cheating! After all, about 30% of respondents will look up the results to factual questions) and self-rated confidence, researchers can build a clearer picture of not just what people think, but how strongly—and wrongly—they hold that belief. And that’s the kind of nuance clients love.
Making the most of misperceptions
We’re not here to tell seasoned researchers how to do their job, but if you’re thinking of adding a few knowledge or misperception elements to your next study, here are some techniques that help keep the experience respectful, insightful, and free from awkward “gotcha” vibes:
Make it feel like a conversation, not a test: The tone of your question matters. Stay neutral and curious, rather than corrective or academic, making it clear that there are no penalties for being wrong and no reward for being correct. Just saying “There are no wrong answers,” doesn’t go far enough.
Give them an out: Including a “don’t know” option can stop respondents from feeling forced to use Google to backseat respond. Alternatively, ask for their best guess and frame it accordingly (“Even if you’re not sure, take a stab at it. We’re just curious!”). It keeps the pressure low and the responses honest.
Tell people what’s going on: Transparency builds trust. If you’re slipping in knowledge-based questions, say so. Let them know you’re exploring what people know (and don’t know), and that “wrong” answers are just as valuable. It makes respondents feel like participants, not exam-takers.
Show the answers at the end: Most people love a good quiz—as long as they get to see how they did. Revealing the correct answers post-survey can add a layer of engagement and even a bit of learning, without undermining the respondent.
Mention it upfront: If you plan to show the answers later, say so from the start. It reassures participants that this isn’t a trick test and reduces the temptation to slip in a sneaky side search or straight up Google their way through. It’s about perception, not perfection.
Pair knowledge checks with attitudinal questions: Juxtaposing what people believe with what they actually know can generate rich insight, not because one is more valid than the other, but because the gap between them tells a deeper story.
Layer in confidence ratings: Confidence doesn’t equal accuracy, but it can highlight when a belief is deeply held, even if it’s entirely off-base.
Keep it light, keep it strategic: A few well-placed misperception checks are enough. After all, you’re not writing a final exam. You’re surfacing subtle, context-rich clues that can enhance how your data is interpreted.
AI and misperceptions: The human touch
Now AI and human misperceptions have both one big thing in common and one big thing that sets them apart.
AI is often, very, very confidently incorrect. Remember when we tried to get ChatGPT to count? Now, tools that have a more specific role than a LLM and are only using the data you’ve provided don’t tend to run into this problem. But whether it’s right or wrong, AI doesn’t care. AI doesn’t believe in anything. The gap is just that, a gap.
That’s what makes misperceptions so profoundly human. Bias, belief, misunderstanding; these aren’t bugs in the system. AI can tell you what, but humans can tell you why, because we’re often invested in our beliefs. That little difference in emotion is what separates us from robots and gives insight into the human experience. And if you want your research to reflect the real world, you’ve got to follow that path. Because an incorrect stat to AI can be replaced, but for a human it reveals something about who they are and how they might act.
The future of knowledge testing
It’s not about proving people wrong; it’s about understanding the cognitive scaffolding that supports their attitudes and decisions. And when done well, this approach doesn’t just elevate your insights—it makes your research feel smarter, deeper, and more attuned to how people operate in the world.
Because, as every good researcher knows, the most interesting data isn’t always what people tell you. It’s what lies between the lines.
