The n = 25, p = 0.5 sampling distribution satisfies the success-failure condition (np = 25 x 0.5 = 12.5) but the n = 10, p = 0.5 sampling distribution does not (np = 10 x 0.5 = 5). Comparing the two distributions, there are two noticeable differences in their shapes: the n = 10 distribution is wider and more discrete than the n = 25 distribution. Which of these two differences is the reason we can't rely on the Central Limit Theorem for the n = 10 distribution? Wider More discrete