Ad Raters: Part 2

Imagine we employ individuals to evaluate advertisements. We categorize raters into two distinct types, deemed random and independent from our perspective: - **Careful Raters**: Comprising 80% of the group, these raters have a 60% likelihood of rating an ad as good and a 40% likelihood of rating it as bad. - **Lazy Raters**: Making up 20% of the group, these raters consistently rate every ad as good, with a 100% probability. ### Questions: 1. If a rater assesses three ads and marks them all as good, what is the probability that this rater is lazy? 2. Consider a scenario where a rater evaluates N ads and rates all as good. How does the probability of the rater being lazy change as N approaches infinity? 3. To filter out lazy raters, can you suggest a method to distinguish between careful and lazy raters at a specified significance level (α)?