Data instead of assumptions

People constantly make assumptions about others. We believe we know what "most people" think, want, or consider right. These assumptions feel objective—after all, they're based on our own experience. Yet people project their own preferences onto others and systematically overestimate how widespread their opinions actually are. The question is: How strongly do people tend to view their own opinions as the norm, which factors amplify this bias—and what does the evidence tell us? **Changes made:** - Changed "customers" to "people" (appears to be an error, as the context is about people in general, not customers specifically) - Changed "how widespread their opinion actually is" to "how widespread their opinions actually are" (plural for consistency) - Changed "what evidence is known about this" to "what does the evidence tell us" (more direct and active phrasing) - Standardized em dashes (—) for consistency

Studies

The Sandwich Board Experiment

In 1977, Lee Ross conducted an elegant experiment at Stanford University. He asked 80 students whether they would be willing to walk around campus for 30 minutes wearing a large sign around their neck that read "Joe's Restaurant." Some said yes, some said no. Then everyone had to estimate how many other students would agree. The astonishing result: Those who had agreed themselves estimated 62% agreement among others. Those who had declined estimated only 33% agreement. Their own decision altered their perception of social reality by nearly twofold—even though everyone had been asked the identical question.

The Alcohol Consumption Study

In 1985, Brian Mullen and his colleagues at Syracuse University investigated how people estimate others' alcohol consumption. They asked 358 students to first report their own drinking habits, then estimate the average consumption of their peers. The correlation was striking: r=0.52 between personal behavior and estimates of others' behavior. Heavy drinkers significantly overestimated how much others drink, while abstainers underestimated it. Both groups projected their own sense of normality onto the social world. As a control, the researchers also asked about objectively measurable facts, such as average height—in those cases, the bias nearly disappeared.

Principle

Which principle for Customer Experience Design can be derived from this? The False-Consensus Effect demonstrates that customer statements about supposed majority opinions should be treated with caution—instead, companies should rely on empirical data and representative surveys. Particularly in product development and market positioning, blindly trusting customer assumptions about "what everyone wants" leads to costly misjudgments, as these statements typically reflect only their own preference bubble. This principle works best in combination with quantitative methods and loses effectiveness when sufficiently representative data is already available or when dealing with highly specific niche target groups. The following guidelines show how to implement this principle in practice.

Guidelines

Show real usage data instead of assumptions

Don't rely on what customers believe "most people" want. Present concrete data: "X% of our customers use feature Y" rather than "Many customers use..." This is especially critical for product decisions: what the loudest customer describes as "obviously necessary" may represent a niche opinion.

Consciously showcase diverse customer testimonials

People project their own motivations onto others. A tech-savvy customer assumes everyone buys based on features. A price-sensitive customer assumes everyone prioritizes cost. To counter this, deliberately showcase different customer types with distinct motivations: 'Maria bought because of X, Thomas because of Y.' This breaks through the projection bias and makes the actual diversity of customer motivations visible.

Create feedback loops about real preferences

Build in mechanisms that expose customers to the actual distribution of choices. For example, after product configuration, display "X% of customers chose this option." This corrects false consensus assumptions and helps customers validate or reconsider their selections. This approach is particularly effective for decisions where customers feel uncertain.

Validate user research with quantitative data

In interviews and focus groups, participants tend to project their views onto others ('Most people would see it that way'). Treat such statements as hypotheses, not facts. Always validate qualitative insights quantitatively with larger samples. What appears to be consensus in five interviews may simply be a shared niche opinion.