# 37% Rule: The Math Behind Life's Hardest Choices

*Society, Psychology & Life · Daniel Foss · 2026-07-29*

## TL;DR

The 37% rule is a mathematical guideline for optimal stopping problems, where you must choose the best option from a sequence without revisiting past choices. By rejecting the first 37% of candidates and then selecting the next one that beats all previous ones, you maximize your chance of picking the best, with about a 37% probability of success.

## Key facts

- The 37% rule originated from the secretary problem, a classic optimal stopping puzzle studied since at least the 1950s.
- Mathematician Thomas Ferguson formalized the rule in his 1989 survey paper on the secretary problem.
- The strategy works best when you know the total number of candidates and cannot revisit rejected ones.
- The 37% rule is sometimes cited as a dating heuristic, though real-life romance rarely meets the strict assumptions of the secretary problem.
- The rule gives you roughly a 37% chance of selecting the single best option from a pool of candidates.

## The Dilemma That Haunts Every Decision-Maker

You're standing at the edge of a big choice, maybe you're about to buy a house, switch jobs, or commit to a partner, and that familiar knot tightens in your stomach. The third house you toured checks nearly every box: good light, solid roof, nice neighborhood. You're ready to make an offer tomorrow morning. But as evening settles in, one question gnaws at you: "What if the next house is even better?" That hesitation, the fear that a better option lurks just around the corner, is the universal dilemma that haunts every decision-maker. It's the moment when you need a strategy, not just hope. The **37% rule** offers exactly that: a mathematical lifeline for when you're drowning in "what ifs."

## What Is the 37% Rule? A Simple Strategy with a Surprising Math Backing

The 37% rule is a straightforward decision-making strategy: reject the first 37% of your options, then pick the very next one that beats everything you have seen so far. If no such option appears, you simply choose the last one. This counterintuitive method, which mathematician and statistician Thomas S. Ferguson analyzed in a 1989 survey paper, comes from a classic puzzle called the secretary problem, and its math is surprisingly precise.

Imagine you are shopping for a house. You have a list of properties to visit, one after another, and you must decide on the spot whether to make an offer. If you say yes too early, you might miss a better home around the corner. If you keep waiting, you could end up with nothing. The 37% rule offers a clean way out: spend the first chunk of your search purely observing, without committing. Once you have passed that threshold, grab the first house that outshines every single one you rejected.

The rule works because of a beautiful mathematical limit. As the total number of candidates grows very large, the optimal fraction to reject approaches 1/e, which is about 0.36788, or 36.788%. That is where the name "37% rule" comes from. Ferguson showed that by rejecting the first *m* candidates and then picking the first one better than all of them, your chance of landing the absolute best option is maximized when *m*/*n* equals 1/e. It is a neat, concrete number born from calculus and probability.

According to the original account, the rule can be applied to decisions like buying a house, changing jobs, or dating. But keep in mind that real-life situations rarely meet the strict assumptions of the secretary problem, you might not know the total number of options, or people might say no to you. Still, as a thinking tool, the 37% rule gives you a memorable, math-backed starting point when you are staring at a long list of choices and wondering when to stop.

### The Math Behind the Magic: Why 37 Percent Works

Mathematician and statistician Thomas S. Ferguson, in his 1989 survey published in *Statistical Science*, revealed that the optimal stopping problem has a surprisingly elegant mathematical solution. The core insight is deceptively simple: pick an integer *m*, reject the first *m* – 1 candidates outright, then select the very first candidate who is better than everyone you have seen before. But why does this work, and where does that magical 37% come from?

Let us walk through the logic step by step. Imagine you have decided to reject the first *m* candidates as a learning phase. Now, suppose the very best candidate happens to be in position *m* + 1. You will pick them immediately, a perfect outcome. But what if the best candidate is in position *m* + 2? Then you face a fork in the road. If the candidate in position *m* + 1 is better than all of the first *m*, you will mistakenly choose them and miss the true best. However, if that *m* + 1 candidate is not better than the first *m*, you will reject them and keep looking, eventually reaching the true best at position *m* + 2. The probability of landing in that good scenario is *m*/(*m* + 1).

This pattern generalizes beautifully. If the best candidate is at position *n*, your probability of selecting them is exactly *m*/(*n* – 1). As the total number of candidates *n* grows large, the sum of these probabilities for all possible positions of the best candidate converges to a Riemann integral. Ferguson showed that this integral, when maximized, yields the optimal ratio *m*/*n* = 1/*e*, which equals approximately 0.367879, or 36.788%. Rounding up, that is the famous 37% rule.

## Where the 37% Rule Shines, and Where It Stumbles

The 37% rule works beautifully for decisions with clear, rankable options, like choosing an apartment or hiring a secretary, but it often falls apart in the messy world of human relationships. Mathematician and statistician Thomas S. Ferguson laid out the classic solution in 1989, and it's a gem for situations where you can line up candidates, reject the first 36.8% of them, then pick the next one better than all before. That strategy maximizes your odds of landing the best choice, assuming you can compare options instantly and objectively. But real life rarely cooperates with those tidy assumptions.

Think about buying a house. You visit ten properties, rank them in your head, and after the fourth one, roughly 37%, you know what's out there. When the fifth house beats everything you've seen, you make an offer. The math backs you up, and the decision feels clean. Same with changing jobs: you interview at a handful of companies, get a sense of the market, and when a role surpasses the early contenders, you take it. In these scenarios, the 37% rule is a powerful heuristic, a reliable starting point that cuts through hesitation.

But apply it to dating, and the cracks show fast. According to the original account, the rule is often cited as a dating strategy, yet real-life romance rarely meets the strict assumptions of the secretary problem. For one thing, you don't make the decision alone; the other person has to accept you too. You might meet someone who's objectively the best so far, but if they're not interested, your careful math means nothing. And even if they are, knowing someone's true character may take 10 or 100 dates, not the instant judgment the rule demands. Human relationships are hard to quantify, and the slippery variables of behavior and emotion turn tidy probabilities into guesswork.

Contrary to the popular version that paints the 37% rule as a universal love hack, records suggest it's best kept for situations where options are plentiful, comparable, and passive, like shopping or hiring. In matters of the heart, use it as a loose guide, not a formula. The rule shines brightest when the stakes are clear and the choices are yours alone to make.

### Who Discovered the 37% Rule?

The formal solution to the secretary problem, and the 37% rule it produced, was laid out in a landmark 1989 survey paper by mathematician and statistician Thomas S. Ferguson, published in the journal *Statistical Science*. Ferguson didn't invent the puzzle itself, which had been circulating under names like the marriage problem since at least the 1950s, but he gave it a crisp, modern mathematical treatment. His paper showed that the optimal strategy boils down to a single elegant number: reject the first 36.788% of candidates, then choose the very next one who beats everyone you've seen so far.

The problem's roots go deeper, though. Mathematicians had been toying with similar "optimal stopping" puzzles for decades. The classic version goes like this: you're interviewing a known number of applicants, one by one. After each interview, you must decide immediately, yes or no, with no chance to go back. You want the best person, not just a good one. The trick is knowing when to stop looking and start choosing.

Ferguson's 1989 paper laid out the math with remarkable clarity. "This basic problem has a surprisingly simple solution," he wrote. He showed that the optimal fraction of applicants to reject approaches 1/e, or roughly 36.788%, as the total number grows large. Why that number? It emerges from summing probabilities and approximating them with an integral, a clean result from calculus that turns a messy real-world dilemma into a crisp rule of thumb. The 37% rule, as it's popularly called, became the go-to answer for anyone facing a sequential choice: reject the first 37% of options, then pick the next one that's better than all the rest.


## Frequently asked questions

**What is the 37% rule?**

The 37% rule is a mathematical strategy for optimal stopping: you reject the first roughly 37% of your options without choosing any, then pick the very next option that beats everything you have seen so far. This approach, rooted in the secretary problem, maximizes your odds of selecting the best candidate from a random sequence. It was formalized by mathematician and statistician Thomas S. Ferguson in a 1989 survey paper.

**How does the 37% rule work for choosing a partner?**

According to the original account, the 37% rule serves as a useful guide for dating, not a guarantee. It suggests you explore and reject the first 37% of potential partners to learn what you value, then commit to the next person who surpasses all previous ones. However, relationships involve mutual choice, the other person must accept you too, and complex personal variables, so the rule is a heuristic, not a surefire formula. Real-life emotions and timing rarely match the strict assumptions of the secretary problem.

**Why is the number 37 percent optimal?**

The optimal fraction m/n equals 1/e, approximately 36.788%. This constant emerges from probability calculations in the secretary problem: as the total number of candidates grows large, the best strategy is to reject the first 1/e fraction. Mathematician Thomas S. Ferguson showed that the sum of these probabilities can be represented by a Riemann integral, and the structure of logarithms and exponential functions produces the value 0.367879. So you observe and reject the first 36.788% of options, then accept the next one better than all previous. By Your Editor, based on mathematical decision theory

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