Clustering Illusions
How genuine randomness produces clumps, gaps, and streaks, and why we so often read them as meaningful or as “not random.”
Human-made Randomness
A fair coin has no memory: each flip is independent. But what does that actually look like over a short sequence? Without flipping a real coin, make up 30 results that you think could plausibly have come from one.
Part 2: Can You Make a Sequence That Looks Like a Fair Coin?
Pretend you're the coin. Make 30 outcomes.
Try to make a sequence that someone else might believe came from a fair coin.
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Human randomness often looks a little too tidy. When people try to imitate chance, they commonly switch outcomes more often and avoid repeats. A fair coin does neither: it has no memory and no preference for what should come next.
Next: what streaks does real randomness actually produce?
Research behind this
The math is exact. With 30 outcomes there are 29 points where one flip meets the next, and for a real coin, the number of those points where the result switches follows a known probability curve, so every comparison above uses the true odds rather than a computer simulation. The experimental findings come from watching how people actually produce random-looking sequences: across many studies, people asked to fill in heads and tails by hand tend to switch more often, and avoid long runs, more than a real coin would. The wider interpretation stays cautious on purpose. A single 30-outcome sequence is a small sample, and details of the task (the instructions given, the pace, whether people can see their earlier choices) are known to affect the result, so this exhibit is meant to teach the idea, not to diagnose any one visitor.
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Further reading: Human judgment of randomness on Wikipedia.