In a similar manner with the Binomial, Geometric, and Negative Binomial distributions, you will remember that each required a known value for p before proceeding with any calculations. From our experiments we saw that relative frequency appeared to stabilize around what you might expect for the true proportion of success and therefore estimating the unknown proportion of success p using relative frequency
\begin{equation*}
p \approx \tilde{p} = \frac{y}{n}
\end{equation*}
where y is the number of successes in a collection of n bernoulli trials. Again, notice that the relative frequency \(\tilde{p}\) is technically an average as well so the probability that a given relative frequency will like exactly on the actual value of p is again zero.