# When to use Binomial versus Beta distribution?

## And what is the point of probability distributions anyway?

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A footballer is known to score 70% of the penalty kicks he shoots. In the next season we expect him to shoot 10 penalty kicks, how many of them will he score?

He will score 7 out of the 10 penalty kicks, obviously!

Actually, 10 penalty kicks is a very little number to make a definite conclusion from. This obvious 7 could turn up to be 8 or 9 with a bit of luck, or he can miss a couple of unexpected penalties and the 7 turns out to be 5. Obviously, huh?

With such a small number, there are hardly any obvious answers, we rather need to express our belief in the form of a distribution.

And in this case, it is a binomial distribution that we need.

## This is what the above distribution represents:

Say we manage to convince this football player to shoot 10 penalty kicks, then ask him to shoot another 10, then another, up to 1,000 sets of 10 penalty kicks. Each time we calculate how many shots out of the 10 he scored, and create a histogram out of it. That’s what we have here.

Keep in mind, the player can never score 7.7 penalty kicks, or 3.8 kicks, only integers are…

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I write about what machines can learn from data, what humans can learn from machines, and what businesses can learn from all three.