You're Not Random | Bites

Think of a Random Number

Pick any number between 1 and 100. Choose one that feels truly random to you, something that no one else would guess. Don't tell anyone. Got it in your head?

Did You Pick 37?

If so, you're in very good company. This is the single most common choice when people try to pick a "random" number. It feels irregular, prime, and awkward, which is precisely why it's so predictable. Magicians call it "the 37 force."

7, 17, 73

The 3 & 7 Family

Numbers with 3 or 7

Humans disproportionately favor numbers containing 3s or 7s. These digits feel "quirky" and unpredictable, making them seem more random. But this preference is so common that 7, 17, 27, 67, 73, and 77 consistently rank among the top choices.

13, 23, 47

Prime Preference

Prime Numbers Feel Random

Prime numbers feel mathematically special and less obvious, so we gravitate toward them unconsciously. Numbers like 13, 23, 29, 41, 47, 53, 67, 71, 79, 83, and 89 are picked far more often than their composite neighbors.

30-70

Mid-Range Bias

Avoiding the Edges

Most people pick numbers in the middle third of the range. We instinctively avoid extremes like 1-10 or 90-100 because they feel "too obvious." True randomness doesn't care about edges, but we do.

10, 50, 100

Usually Avoided

Round Numbers Seem "Too Easy"

We systematically avoid round numbers like 10, 20, 25, 50, 75, and 100. They feel too neat and predictable. If you picked one of these, you're bucking the trend (though still not truly random).

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The Takeaway

You're Predictably Unpredictable

Whatever you picked, you followed a pattern. We think randomness should look irregular, so we avoid patterns, which creates its own predictable pattern. True randomness is flat and boring. Human randomness is lumpy and fascinating.

True Randomness vs. Human Randomness

When a computer generates random numbers, they're uniformly distributed. When humans do it, patterns emerge. Compare the distributions below to see how predictable we really are.

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True Random Distribution

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Human "Random" Distribution

The Psychology of Predictable Randomness

When you try to act randomly, you reveal more about yourself than you realize. Humans are remarkably poor at generating truly random numbers, and our failures follow surprisingly consistent patterns that expose fundamental quirks in how our brains work.

The number 37 is famous among magicians and psychologists as "the most random number." When asked to pick a random number between 1 and 100, a disproportionate number of people choose 37 or its close cousins like 7, 17, 73, and 67. Why? Because these numbers feel random to us. They're prime, they're odd, they're not too close to the edges (1 or 100), and they're not suspiciously round like 50 or 25. Our intuition tells us that randomness should look irregular and unpredictable, so we gravitate toward numbers that seem quirky and hard to guess.

But here's the paradox: this very instinct makes us extraordinarily predictable. True randomness is flat. Every number has an equal chance. But human "randomness" is lumpy, with clear peaks and valleys. We systematically avoid extremes, round numbers, and multiples of 5 or 10. This phenomenon, documented extensively in psychology research, is known as subjective randomness bias: the gap between what randomness actually looks like and what we think it should look like.

The takeaway is both humbling and fascinating: when you try to be random, you're not. You're revealing your predictability. The number 37 isn't the most random. It's the most predictable. We're pattern-seeking creatures in a world that sometimes demands patternlessness, and we struggle every time we try to escape our own cognitive defaults.

Why This Happens

Our failure to generate randomness isn't a flaw. It's a feature of how our minds evolved. Three core psychological mechanisms drive our predictable patterns when we try to be unpredictable.

Pattern Recognition Overload

Our brains evolved to detect patterns for survival: spotting predators, finding food, recognizing faces. This makes us hypersensitive to regularity, causing us to actively avoid patterns even when trying to be random. Ironically, this avoidance creates its own predictable pattern.

Availability Heuristic

Numbers that "come to mind" easily feel less random. We avoid obvious choices like 1, 50, or 100 because they seem too simple. Instead, we pick numbers that feel obscure, which paradoxically makes them common choices among people with the same bias.

Misunderstanding Randomness

True randomness includes clusters, repetitions, and patterns by pure chance. But we expect randomness to look evenly distributed, so we artificially space out our choices. This is why people think a fair coin "shouldn't" land heads five times in a row, even though it absolutely can.

These biases aren't bugs in our cognitive system. They're deeply embedded features that have served us well for survival. But they also mean that whenever we try to generate randomness manually, we're fighting against millions of years of evolution.

Real-World Implications

Understanding human randomness bias has practical applications across multiple domains:

Security & Cryptography: Password generators never rely on human intuition because our choices are too predictable. Secure randomness requires algorithmic generation with high entropy.
Game Design: Shuffle algorithms deliberately avoid true randomness to feel "more random" to players. Spotify's shuffle, for example, spaces out artists to prevent perceived repetition.
Statistics & Sampling: Random sampling must be algorithmic; humans unconsciously introduce selection bias when trying to pick "random" samples.
Machine Learning: Understanding human bias helps detect fraud, predict behavior, and design systems that account for our predictable unpredictability.

References & Further Reading

This phenomenon is well-documented in psychology research, where studies on subjective randomness show we systematically avoid extremes and round numbers. Key research includes Nickerson's work on the production and perception of randomness (2002), Griffiths and Tenenbaum's algorithmic models of human randomness (2004), and practical explorations by Hargreaves on randomness in game development (2009).

For deeper exploration, see: Nickerson (2002) - The production and perception of randomness, Griffiths & Tenenbaum (2004) - Algorithmic models of randomness, and Hargreaves (2009) - The psychology of randomness in game development.

This bite was inspired by Veritasium's video "Why is this number everywhere?", which explores the fascinating psychology behind why 37 appears so frequently when people try to be random.