Representativeness Heuristic: The Truth About Your “should be”

The representativeness heuristic is a mental shortcut we use when estimating probabilities. When we’re trying to assess how likely something is, we often make a decision by assessing how similar it is to an existing prototype. For example, say we know our friend will introduce us to a computer scientist and a music producer. We greet them: one is wearing glasses and carrying their laptop with them, the other a graphic shirt with a rock band on it. The representativeness heuristic tells us the one with glasses is the computer scientist. But we later find it was the other way around.

The error above is because a person’s appearance is representative of a prototype we hold. In this case, it is that of a computer scientist: a nerdy appearance and the laptop is a cherry on top. Such shortcuts can be useful in many cases, but result in errors of judgment otherwise. Heuristics are “shortcuts that generally get us where we need to go but at the cost of occasionally sending us off course.” The reality is that just because something is more representative—a nerd computer scientist over a nerd music producer—does not make it more likely.

Since we rely on how representative something is, we fail to consider all the information. The result? Poor predictions, choices, and solutions. This can easily tip into prejudice. Take, for instance, how mass media portrays minority groups, reinforcing commonly-held stereotypes. Black men are overrepresented in coverage of crime, bad education, and poverty. White alternatives are overrepresented as thought leaders and experts. These patterns support a narrative: black men are violent, while white men are trusted leaders. Rep this belief enough, and it contributes to systemic discrimination. Police focus disproportionately on black individuals in their searches. Employers are less likely to hire black-sounding names. Countless other instances which I’ll leave to your imagination.

Representativeness is also pervasive in products and services. Designers incorporate symbols that represent categories and functions, without us even realizing it. A star, heart, or thumbs-up typically means marking something we like. An arrow back symbolizes exactly that: to go back. The “X” is an icon for turning something off, and the plus is used for creating stuff—tabs, posts. These prototypes make navigating the virtual space vastly easier. This is a case of where it works in our favor. But it doesn’t always. The worst is perhaps that it results in discarding uniqueness: by filtering ourselves, others, and situations into existing buckets, we fail to account for one-off cases.

Real-world implications and what you should do the representativeness heuristic:

• This is a mental shortcut which pushes you to assess the likelihood of something based on how it fits an existing stereotype. If a coin lands on heads five times in a row, many believe it “must” hit tails next—even though the probability hasn’t veered from the 50 percent. The past pattern feels “representative” of what “should” happen. If somebody is quiet, introverted, and loves books, you are more likely to take them for a librarian than a salesperson. Your prototype of what a librarian is fits the description. This can get awry when a doctor assumes that a young, fit person is unlikely to have cancer. Only to later find out once it has spread beyond control that the symptom-based diagnosis was indeed correct.

• The brain prefers pattern recognition over statistical reasoning. This is because: (1) it saves mental energy through quick judgments; (2) it feels intuitive as the judgments are based on resemblance and seem obvious; (3) it’s often right, as many things that share similarities do look alike. But it ignores actual probability. Always check the stats before making assumptions. It results in stupid errors, like the Linda problem. Linda is 31, outspoken, and deeply concerned with social justice. She was a philosophy major. Which is more likely: she is a bank teller; she is a bank teller and activist. Most pick the latter because Linda represents an activist stereotype. The problem is that it is mathematically impossible: there are more bank tellers than there are bank tellers who are also activists.

• Places to use it—to watch out for it: business, investing, marketing, consumer behavior, justice, and healthcare. In business it helps make quick hiring and investment decisions. Do be careful though. Rely on track record. Compare against industry averages, not past success stories. In marketing, people assume products that resemble luxury brands are high-quality. They overpay for knock-offs or buy into branding without genuine value. Check reviews and materials before you fall for the same trap. In justice, quick pattern recognition can help detect crime. But it results in profiling errors when innocent people are suspected since they represent criminal prototypes. Base actions on evidence, not prevalence.

How to use the representativeness heuristic like a pro:

(1) recognize when you’re falling for it. Ask: Am I judging based on resemblance, or actual data? Is this person really “dangerous” or do they remind me of a movie villain? Is this investment really “safe” or does it feel like a good bet?

(2) use base rates and statistic reasoning instead of gut feelings. Look up actual probability rates before making a decision. If 90 percent of startups fail, assuming yours will succeed because it “feels” different is ignorance of statistics.

(3) beware of over-specificity. Adding details makes this feel more probable—the Linda problem—but in reality, details make outcomes less probable. If somebody describes an investment as a “once-in-a-lifetime tech opportunity led by a visionary founder” you can quickly infer that, however promising it sounds, the description doesn’t change the genuine risk profile.

(4) avoid “looks like”, “should”, and “appears to be” judgments in high-stakes decision-making. Hiring: are you choosing a candidate because of their actual skills or because they “look like” a leader? Investing: are you betting on a stock because it “appears to be” a grower or based on actual business fundamentals? Medical diagnosis: is the doctor considering all the possibilities or dismissing options based on a “typical patient”?