A French trader named Theo bet $20 million on Trump winning the election – and walked away with $50 million in profit.
How?
He didn’t trust the traditional polls.
Instead, he found survey data where researchers asked a simple but brilliant question: “How do you think your neighbors are going to vote?”
That indirect question revealed what direct polling couldn’t – people’s actual voting intentions they were uncomfortable sharing publicly.
This got me thinking about all the ways our marketing quizzes and surveys can mislead us into expensive mistakes.
I’ve been writing about survey dangers for years, and I’ve seen countless marketers waste serious money by taking quiz responses at face value.
The truth is, great research is damn hard to execute.
If professional pollsters with million-dollar budgets get blindsided by bias, what hope does the average marketer have?
But here’s the thing – you don’t need perfect research.
You just need to understand the hidden forces warping your data and know how to work around them.
Let’s dive into the five sneaky biases that are probably screwing with your quiz results right now…
1. Marketer’s Bias: When You’re Too In Love With Your Idea
I’ve been guilty of this one myself, and it nearly cost me big.
We were working on a canned meat product (don’t judge), and our research “showed” there was a decent market for it.
The problem? We were so in love with our concept that we completely missed the warning signs.
Here’s what happened: people happily ate our free samples and said nice things about them.
But when it came time to actually pay for the product? Crickets.
When you fall in love with your idea, you either design your quiz to validate what you already believe, or worse – you discount negative feedback as a “mistake” in your methodology.
This is especially dangerous with lead gen quizzes.
You craft questions designed to guide prospects toward your solution, consciously or unconsciously ignoring signals that indicate your product might not be what they need.
It’s human nature. You’ve invested time, energy, and often your own identity into your idea. Of course you want it to succeed!
So when your quiz shows low completion rates or weird answer patterns, you rationalize: “The quiz is too long” or “People don’t understand the question” – rather than facing the uncomfortable truth that your offer might need work.
I’ve watched marketers completely redesign their quiz flow and UI when the real problem was their core offer.
They kept tweaking the vehicle instead of questioning the destination.
The danger multiplies when you’re creating quizzes for clients.
They fall in love with their idea, then you fall in love with your quiz design, creating a double-layer of bias that practically guarantees skewed results.
The solution?
Consider bringing in a third party to conduct your research.
Remove yourself from the equation so your own biases don’t contaminate the results.
And if that’s not possible, deliberately include questions that might disprove your hypothesis. Force yourself to look for evidence that you’re wrong.
Remember: your quiz should uncover market truth, not just validate what you already believe.
2. Response Bias: When Engagement Doesn’t Equal Intent
You’ve seen this online countless times. You post something and get loads of likes and loves… but those engagers aren’t actual buyers.
It’s like those Facebook friends who never interact with your posts publicly, but mention them when you meet up in person.
Some people just love hitting that like button, while others consume content silently.
The people most willing to take your quizzes are often the least likely to buy your products.
Think about it – who has time to complete a 10-question quiz about “What Type of Entrepreneur Are You?”
The busy, successful entrepreneur who needs your services, or the wannapreneur scrolling social media for motivation?
Response bias creates this weird paradox where the people filling out your quizzes might be fundamentally different from your ideal customers.
Some personality types just love completing quizzes and surveys, while others (often the decision-makers with purchasing power) avoid them entirely.
I’ve seen marketers get completely demoralized when their brilliant quiz gets minimal engagement.
They scrap the whole concept, not realizing that their target market just communicates differently.
Meanwhile, a competitor launches a simple PDF download that captures the same audience because it matched how those particular buyers prefer to engage.
This works both ways. Sometimes your quiz gets tons of completions from people who will never buy.
They’re just quiz junkies who love getting categorized and seeing results.
They’ll happily tell you they’re “definitely interested” in your high-ticket coaching program, then disappear forever when you follow up.
The key to fighting response bias?
Cross-reference quiz engagement with actual sales data.
Look for patterns in who completes versus who converts.
Sometimes your best prospects might bail on question three but still book a call from your partial completion email sequence.
3. Sampling Bias: Your “Random” Sample Isn’t Random At All
Here’s the cold truth about online sampling: it’s rarely ever truly random, especially with ad networks.
When you validate concepts using ads, the algorithms are designed to find people most likely to respond positively.
Facebook isn’t showing your survey to a representative sample – it’s showing it to people its AI thinks will engage.
This creates a dangerous feedback loop.
You create a quiz targeting entrepreneurs. The algorithm finds quiz-taking entrepreneurs.
They complete your quiz. You think “great, my ideal customers love this quiz!” – except you’ve only reached a tiny, unrepresentative slice of your actual market.
I’ve watched marketers congratulate themselves on their “market validation” when all they’ve really done is find the 2% of their audience that the algorithm determined would like their content.
Meanwhile, they’re completely missing the 98% who might be perfect customers but never saw the quiz.
This sampling bias gets worse the more you optimize.
Say your quiz about “Discover Your Marketing Archetype” starts performing well with female entrepreneurs aged 35-45.
The algorithm doubles down, showing it to more people in that demographic.
Soon, you’ve convinced yourself your entire market is female entrepreneurs in their early 40s because that’s all you see in your quiz results.
But what if your best customers are actually 55-year-old male business owners who rarely take quizzes but spend 3x more when they do buy?
You’ll never know because your sampling method systematically excludes them.
The ad networks compound this problem.
When you place an ad on Google or Facebook, the platform reads your copy and finds the audience most likely to engage – not necessarily the audience most representative of your total market.
This might dramatically understate your true market size by only collecting responses from those most likely to respond.
4. Social Desirability Bias: What People Won’t Tell You Directly
This is the one that “Trumped” the US polls. People hide or don’t reveal their true views when they feel social pressure.
People will lie in your quizzes if telling the truth makes them look bad – even to an algorithm they’ll never meet.
Think about your weight loss quiz asking “How many times a week do you exercise?”
Nobody wants to select “zero” – even when that’s the honest answer.
Or your financial quiz asking about debt levels. Your marketing quiz asking about revenue goals. Your productivity quiz asking about work hours.
The truth? People constantly misrepresent themselves in quizzes.
I saw this firsthand with a client’s health quiz.
When we asked directly about alcohol consumption, nearly everyone selected “1-2 drinks weekly” – the socially acceptable answer.
But when we reframed it as “What’s your go-to evening relaxation?” and buried “glass of wine” among other options, we got dramatically different results.
The indirect question revealed consumption patterns closer to 5-7 drinks weekly for many respondents.
This bias hits hardest when your quiz touches anything potentially embarrassing:
- Weight/fitness level
- Income/financial status
- Business success/failure
- Knowledge gaps
- Personal habits
- Political views
- Buying motivations (status vs. practicality)
Even in supposedly anonymous online quizzes, people remain skeptical about true anonymity.
They know their email is attached to their answers. They worry you’re judging them. They worry about data breaches exposing their responses.
The French trader who made $50 million understood this bias perfectly.
The polls weren’t capturing Trump’s true support because respondents felt social pressure to give the “acceptable” answer.
By asking about neighbors instead, researchers removed that pressure and uncovered hidden preferences.
Your quizzes likely suffer from the same problem.
If national polls with massive resources consistently get this wrong, your marketing quiz almost certainly has this issue too.
5. Hypothetical Bias: The Gap Between “Would” and “Will”
Hypothetical bias is when people don’t actually behave the way they claim they would in hypothetical scenarios.
“How much would you pay for this product?”
“I’d totally pay $10 for that!”
“Great! Here’s the payment link…”
“Oh, well, I need to think about it first…”
This bias destroys the accuracy of nearly every lead gen quiz I’ve ever seen.
People will happily select that they’re “very likely” to invest in your high-ticket coaching program when it costs them nothing to click that option.
But when faced with the actual decision to part with real money? Their behavior rarely matches their hypothetical intentions.
I’ve seen this play out painfully with clients who designed entire product launches based on quiz responses indicating “strong interest” in their new offering.
They built complex funnels, spent thousands on production, and ended up with a fraction of the sales their quiz data predicted.
The quiz respondents weren’t deliberately lying – they genuinely thought they would buy.
But hypothetical interest doesn’t translate to actual purchasing behavior.
This bias shows up most clearly in:
- Pricing questions (“What would you pay for…”)
- Timeline questions (“When would you implement…”)
- Commitment questions (“How likely are you to…”)
- Effort questions (“How much time would you invest in…”)
The problem gets worse when your quiz uses hypothetical scenarios to segment leads.
“Imagine you just got a $10,000 bonus. Would you: A) Invest it, B) Pay off debt, C) Take a vacation?”
The answers tell you almost nothing about how someone would actually behave with real money on the line.
This same problem plagues recruitment.
Ask candidates hypothetical interview questions, and they give you perfect hypothetical answers.
“How would you handle a difficult client?” produces idealized scenarios that bear little resemblance to how they’d actually behave under pressure.
The solution? Stop asking hypothetical questions altogether.
Instead, ask about past behavior: “What’s the most you’ve spent on professional development in the last year?”
Historical actions predict future behavior far better than hypothetical intentions.
Instead of “Would you buy X at Y price?” try “What similar products have you purchased in the past six months?”
Past spending patterns reveal actual buying behavior, not wishful thinking.
The “Neighbor Technique”: How Indirect Questions Reveal Hidden Truth
Remember that French trader, Theo, who made a $50 million profit betting on Trump’s victory?
His edge came from understanding survey psychology rather than political science.
Theo discovered a fascinating survey that didn’t ask people directly who they planned to vote for.
Instead, it asked: “How do you think your neighbors are going to vote?”
This simple shift from direct to indirect questioning completely transformed the results.
Why?
Because it removed the social pressure from respondents.
They could reveal their true intentions while feeling like they were talking about someone else.
When you ask people directly about sensitive topics, you get their public face. When you ask indirectly, you get closer to their private truth.
The theory is brilliant and psychologically sound: people are more likely to reveal their actual preferences when they feel they’re talking about someone else.
It takes them off the hook. “I’m not telling you about myself – I’m talking about my neighbor!”
When Theo compared these indirect results with traditional polls, he spotted a massive gap – and bet $20 million on that insight. The rest is history.
Now, is this technique foolproof? No.
It bombed in the last Indian election. But it’s another valuable tool in your quiz-building arsenal.
Here’s how to apply the neighbor technique to your lead generation quizzes:
- Instead of “How much would you pay for this service?” try “What do you think most businesses your size would pay for this service?”
- Replace “What’s your biggest marketing challenge?” with “What marketing challenges do you see other companies in your industry struggling with most?”
- Switch “Would you be interested in a high-ticket coaching program?” to “Which of your colleagues would benefit most from intensive coaching?”
These indirect questions often reveal the respondent’s own thoughts while bypassing their self-censorship filters.
Validating Quiz Data Without Getting Burned
So what’s the answer? How do you create quizzes that actually predict buyer behavior rather than collecting misleading data?
The first solution is to look at current market data – how much money is actually being spent in your space.
This is where comparative research becomes valuable.
If someone’s already running a successful quiz funnel in your market, that’s solid evidence that:
- There’s real money flowing in this space
- The audience responds to quiz-based lead generation
- There’s a proven path to monetization
Looking at market size isn’t about copying someone’s campaign – it’s about validating that there’s genuine demand backed by real spending.
In my consumer goods days, we’d buy retail data from Nielsen to understand category performance.
We’d analyze 5-7 years of data to spot trends: Which segments were growing? Shrinking? Stagnant?
Interestingly, we often targeted stagnant markets where competitors were making “free money” – stable demand with minimal marketing activity.
These proved easier to disrupt than highly competitive growing segments.
The same principle applies to your quiz strategy.
Sometimes the best opportunity isn’t the hottest market but the overlooked one where basic innovation still works.
Learning from Real-World Examples: The Market Testing Edge
One major advantage of online quizzes over traditional surveys is the ability to test multiple variations simultaneously at relatively low cost.
Unlike launching a physical store or product line, online experimentation costs pennies on the dollar.
This is the critical edge that online marketers have over traditional businesses.
While a restaurant might invest millions in location, staff, and inventory before getting a single customer response, you can test five different quiz concepts for a few hundred dollars each.
The key is designing these tests to neutralize the biases we’ve discussed:
- Test concepts you personally dislike to counter marketer’s bias
- Measure actual conversion rather than engagement to fight response bias
- Use multiple traffic sources to minimize sampling bias
- Include both direct and indirect questioning to detect social desirability bias
- Focus on past behavior rather than future intentions to avoid hypothetical bias
The online world forgives experimentation in ways the physical world never could. Your biggest risk isn’t testing – it’s failing to test enough variations.
We Can Make Better Quizzes
Your quizzes are probably lying to you – but that doesn’t mean they’re useless.
By understanding these five critical biases, you can design smarter quizzes that reveal actual market preferences rather than misleading signals.
Remember:
- Marketer’s Bias: Bring in outside perspectives to challenge your assumptions
- Response Bias: Consider who isn’t taking your quiz, not just who is
- Sampling Bias: Recognize that algorithms create non-random samples
- Social Desirability Bias: Use indirect questions for sensitive topics
- Hypothetical Bias: Focus on past behavior rather than future intentions
The French trader who made $50 million didn’t invent a revolutionary polling system.
He simply recognized the limitations of traditional methods and leveraged an alternative approach that addressed a well-known bias.
Your opportunity is similar.
While your competitors continue building quizzes based on flawed assumptions, you can design instruments that actually predict buying behavior by accounting for these psychological realities.
The most valuable quiz isn’t the one with the highest completion rate or the prettiest design.
It’s the one that accurately identifies people who will actually buy from you.
