Hypothesis testing and the binomial distribution

So now we know some background on hypothesis testing and the difference between one-sided and two-sided testing, let's apply this knowledge to one of the most common distributions: the binomial distribution. To recap, the binomial distribution deals with discrete random variables and it gives probabilities for counts with binary data.

Let's go back to an example we worked with in earlier labs: the exam of 25 multiple choice questions. Is each question has 5 options, it would make logical sense that the probability of guessing 1 question correctly is 0.2. Now suppose we have a student who answered 12 out of 25 correctly and we believe that this student did better than merely guessing. What could be our corresponding hypotheses?

Possible Answers

\(H0: p = 0.20, H1: p \neq 0.20\)
\(H0: p = 0.20, H1: p > 0.20\)
\(H0: p = 0.20, H1: p \leq 0.20\)

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Hypothesis testing and the binomial distribution

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