Base Rates and Base Rate Neglect: The Taxicab Problem Explained

What is a base rate?

A base rate is how often something happens in the relevant population, before you know anything about the particular case. If about one in ten restaurants in your area closes within its first year, that is a base rate for “will this new restaurant survive year one?”

Base rate neglect, also called the base rate fallacy, is ignoring that number. Given a specific story (a talented chef, a great location), people judge by how convincing the story is and forget to ask how often stories like it end well. The fix is simple to state: start with the base rate, then adjust for the specifics.

As a mental model, base rates are your default starting point for any prediction about a case: a hire, a project, a diagnosis, an investment.

Where it comes from

The idea is old (it is built into Bayes’ theorem from the 18th century), but the psychology of ignoring it comes from Daniel Kahneman and Amos Tversky. In the 1970s they showed that people judge probability mainly by representativeness: how much a case resembles a typical example. When a description sounds like an engineer, people say it is probably an engineer, even when told the group contains far more lawyers.

Their best-known demonstration is the taxicab problem:

A cab was involved in a hit-and-run at night. Two cab companies operate in the city: 85% of cabs are Green and 15% are Blue. A witness identified the cab as Blue. Tested under similar conditions, the witness correctly identifies each color 80% of the time. What is the probability the cab was Blue?

Most people answer 80%, the witness’s accuracy. The correct answer is about 41%. Here is why, imagining 100 accidents:

• 15 involve Blue cabs. The witness correctly says “Blue” for 12 of them.
• 85 involve Green cabs. The witness wrongly says “Blue” for 17 of them (20% of 85).
• So “Blue” is said 29 times, and only 12 of those are right. 12 ÷ 29 ≈ 41%.

The witness is good, but Green cabs are so common that the witness’s errors on them outnumber the correct Blue calls. Kahneman and Tversky also found that when the base rate was given a causal framing (Green cabs cause more accidents), people used it much more. We ignore statistics but listen to stories.

Kahneman later connected this to the “outside view”: forecasting a project by looking at how similar projects went, rather than at the details of yours. That is base rates applied to planning.

How to use base rates

1. Name the reference class. What group does this case belong to? “First-time founders’ startups,” “kitchen remodels in my city,” “candidates hired from referrals.” Pick a class broad enough to have data and narrow enough to be relevant.
2. Find or estimate the base rate. Look up data if it exists. If not, estimate from experience and write the number down before reading more details.
3. Read the specific evidence. Now look at the case: the résumé, the plan, the test result.
4. Ask how diagnostic the evidence is. How much more likely is this evidence if the outcome is true versus false? Weak or common evidence should move you a little; strong, rare evidence can move you a lot.
5. Adjust from the base rate, not from zero. Your final estimate should start at the base rate and move toward the evidence. If you land far from the base rate, write down why.

This is the backbone of probabilistic thinking, and it feeds directly into expected value calculations.

3 real-world examples

Business

Say a team estimates a software migration will take three months. The plan is detailed and everyone is confident. A base rate check asks: how long did the last five migrations of similar size take, here or at comparable companies? If most ran well past their estimates, the honest forecast starts there. This is the cure for the planning fallacy: the details of your plan feel unique, but the base rate knows how plans like yours usually go. (A hypothetical example.)

Personal life

Medical tests are the most important everyday case. Take a hypothetical screening test that catches 90% of true cases and gives a false positive 5% of the time, for a condition that affects 1 in 1,000 people. In 100,000 people, about 90 true cases test positive, but so do about 5,000 healthy people. A positive result means roughly a 2% chance of having the condition, not 90%. This is why doctors follow a positive screen with a second test rather than a diagnosis.

Investing

A stock pitch arrives with a compelling story: visionary founder, huge market, early traction. The base rate question is how often companies with that profile delivered the returns implied by their price. Remember that the stories you hear are filtered: you hear about the winners, which is the survivorship problem. The base rate includes the companies nobody writes about anymore.

Common mistakes

• Choosing the wrong reference class. “Restaurants” and “restaurants opened by chefs with prior successful restaurants” have different base rates. Too broad loses relevance; too narrow loses data.
• Ignoring the evidence entirely. Base rates are a starting point, not a verdict. Strong, specific evidence should move you.
• Treating “this case is special” as evidence. Every case feels special from the inside. Specialness is what the base rate already accounts for.
• Confusing the two directions. The chance a test is positive if you are sick is not the chance you are sick if the test is positive. The base rate is what connects them.
• Using a stale base rate. If conditions changed (new technology, new regulation), last decade’s numbers may no longer apply.

Base rates vs specific evidence

The Mentarium take

If we could make one change to how people predict outcomes, it would be this: write the base rate down before you read the case. Once you have heard the story, the base rate feels irrelevant, and you will anchor on the details. Writing it first gives the boring number a fighting chance.

In the Mentarium Decision Loop, base rates belong in the Decide step, where you write your prediction and confidence. Your confidence percentage should start at the base rate. If your plan to launch a product says “80% chance of hitting our target” and similar launches hit theirs a third of the time, either you have strong evidence for the gap or your number is a hope. Before any forecast, write three lines: the reference class, the base rate, and what makes your case different. If the third line is empty, your forecast is the base rate.