I don’t agree:

This is a binomial probability problem. Each roll of a fair six-sided die is an independent trial with success probability \(p = \frac{1}{6}\) (rolling a 6) and failure probability \(1 - p = \frac{5}{6}\) (rolling anything else). We want the probability of exactly \(k = 5\) successes in \(n = 8\) trials.

The binomial probability formula is:

\[

P(X = k) = \binom{n}{k} p^k (1 - p)^{n - k}

\]

First, compute the binomial coefficient:

\[

\binom{8}{5} = \frac{8!}{5!(8-5)!} = \frac{8!}{5! \cdot 3!} = 56

\]

Substitute into the formula:

\[

P(X = 5) = 56 \left( \frac{1}{6} \right)^5 \left( \frac{5}{6} \right)^3 = 56 \cdot \frac{1^5 \cdot 5^3}{6^8} = 56 \cdot \frac{125}{1,679,616} = \frac{7,000}{1,679,616}

\]

Simplify the fraction by dividing numerator and denominator by their greatest common divisor (which is 8):

\[

\frac{7,000 \div 8}{1,679,616 \div 8} = \frac{875}{209,952}

\]

This fraction is in lowest terms. As a decimal approximation, this is about 0.00417 (or 0.417%).

Reply to this note

Please Login to reply.

Discussion

The math is beyond me, I will admit

I'd ask AI to write simple-to-understand code to virtually replicate how you rolled the dice a few billions of times and then calculate how many of the results matched what you saw. Then review the code to see if it seems to match what you expect and run it.

Even when I calculate odds myself, I often write a quick script to brute force an approximation for assurance. CPU cycles are cheap.

Pretty sure that formula is for rolling 6, 5 out of 8 times. nostr:npub1trr5r2nrpsk6xkjk5a7p6pfcryyt6yzsflwjmz6r7uj7lfkjxxtq78hdpu mentioned rollings 6s (two dices of 6), 5 out of 8 times.