Imagine you’re a data analyst in a food delivery service. After every order, customers can rate the food’s quality. The team’s primary goal is to deliver top-tier service, and if a restaurant receives bad reviews, you need to check it. So, here’s the big question — how many bad reviews should trigger a restaurant check-up?
Sometimes, a restaurant can end up with some not-so-great feedback just occasionally, and it’s not their fault. If a restaurant has handled 1000 orders, they might get a couple of bad reviews by chance.
Think of it like this: about 5% of orders end up with negative reviews just by chance. Then the number of bad reviews per restaurant follows a Binomial distribution Bin(n, p), with “n” being the orders and “p” the likelihood of a bad review (which is 5% in our case).