The normal distribution emerges from repeated coin flips because combinatorics determines that there are more ways to achieve middle outcomes than extreme ones; for example, with 10 flips, getting 5 heads has 252 possible paths while getting 0 heads has only 1 path, and as the number of flips increases, this geometric advantage of middle outcomes converges to the Gaussian curve.
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Why does the normal distribution look like a curve? #mathsAdded:
Every coin flip is independent. 50/50, no memory.
So, why does repeating it hundreds of times produce something so structured?
It's combinatorics. The number of ways to get exactly K heads from N flips is N choose K.
That's it.
And N choose K is not flat. 10 flips, zero heads has one path. Five heads has 252.
The middle term of the binomial expansion is always the largest.
So, when you histogram the outcomes, the center dominates. Not because of some law of nature, but because there are geometrically more paths to the middle.
Normalize it. Take N to infinity. That's your Gaussian. Because the middle has more paths.
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