This problem can be solved using the binomial probability formula. The binomial probability formula is used to find the probability of getting exactly $k$ successes in $n$ independent Bernoulli trials, each with the same probability of success $p$.

The binomial probability formula is given by:

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

Where:

• $n$ is the number of trials (in this case, $n = 5$),
• $k$ is the number of successes (in this case, $k = 4$),
• $p$ is the probability of success on a single trial (in this case, $p = 0.9$ since 90% are looking for the website),
• $1-p$ is the probability of failure on a single trial (in this case, $1-p = 0.1$).

First, we need to calculate $\binom{n}{k}$, which represents the number of combinations of $n$ items taken $k$ at a time:

$\binom{5}{4} = \frac{5!}{4!(5-4)!} = \frac{5 \times 4 \times 3 \times 2 \times 1}{(4 \times 3 \times 2 \times 1)(1)} = 5$

Now, substitute the values into the binomial probability formula:

$P(X = 4) = 5 \times (0.9)^4 \times (0.1)^1$

Let's calculate this step by step:

1. $(0.9)^4 = 0.9 \times 0.9 \times 0.9 \times 0.9 = 0.6561$
2. $(0.1)^1 = 0.1$
3. Multiply them together: $0.6561 \times 0.1 = 0.06561$
4. Finally, multiply by 5: $5 \times 0.06561 = 0.32805$

So, the probability that exactly 4 out of 5 visitors are actually looking for the website is approximately 0.328 (rounded to three decimal places).

Would you like more details or have any questions?

Here are 5 related questions to expand your understanding:

1. What is the probability that all 5 visitors are actually looking for the website?
2. How would the probability change if the sample size were increased to 10 visitors?
3. What is the expected number of visitors who are actually looking for the website in a random sample of 5?
4. How would the probability change if the percentage of visitors looking for the website increased to 95%?
5. Can the binomial probability formula be applied if the trials are not independent?

Tip: The binomial distribution is useful when you have a fixed number of independent trials, each with the same probability of success.