A hypothesis test using a binomial distribution can determine if the result of only 2 out of 30 students winning is statistically significant evidence against the company's claim of a '1 in 6' chance of winning.
The student's question revolves around the probabilities associated with a promotional event and whether the observed outcomes provide convincing evidence against the company's claim.
Given that a company claims that "1 in 6 wins a prize," we would expect that out of 30 students, about 5 should win a prize (since 30/6 = 5). However, only 2 students got winning caps.
To determine if this is significantly different from the company's claim, a hypothesis test can be conducted.
Using the binomial distribution, we can calculate the probability of 2 or fewer students winning a prize if the true probability of winning is indeed 1/6.
The calculated probability can then be compared to a significance level to determine whether the result is statistically significant. If the observed probability is low (typically less than 5% or 0.05), we might consider the evidence against the company's claim as 'convincing.'