From the formula, \(\mu = np = 40000 \cdot \frac{1}{4} = 10000\text{.}\) So, using Poisson’s probability function
\begin{equation*}
f_{\text{Poisson}} = \frac{10000^{x}}{x!}e^{-10000}
\end{equation*}
would require you to compute
\begin{equation*}
e^{-10000} \sum_{x=10000}^{\infty} \frac{10000^{x}}{x!}
\end{equation*}
which is also a mess. However with a computational resource such as a graphing calculator, just compare 1 - binomcdf(40000,0.25,9999) to 1-poissoncdf(10000,9999) noting that the complement of the given question is from X from 0 to 9999. The two values should be relatively close
This approximation method is not completely satisfactory since both being discrete distributions with no nice distribution function formulas require summations to accumulate. We have seen however that both the Poisson and the Binomial probability functions start to have a bell-shape as \(\mu\) increases for the Poisson and as n increases (i.e. and therefore \(\mu\) increases) for the Binomial. Hence, we will eventually approximate with each of these using the (continuous) bell-shaped distribution--the normal distribution discussed later--for which instead of accumulating probability function values we integrate them.