Step 1 :We are given a simple random sample of 1040 adults, among which 92% rated themselves as above average drivers. We want to test the claim that more than \(\frac{4}{5}\) of adults rate themselves as above average drivers.
Step 2 :We perform a hypothesis test. The null hypothesis (H0) is that the proportion of adults who rate themselves as above average drivers is equal to \(\frac{4}{5}\), and the alternative hypothesis (H1) is that the proportion is greater than \(\frac{4}{5}\).
Step 3 :We use the sample proportion (\(\hat{p}\) = 0.92) and the sample size (n = 1040) to calculate the test statistic and the p-value.
Step 4 :The test statistic (z) is calculated using the formula \(z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}\), where \(p_0\) is the hypothesized population proportion under the null hypothesis. Substituting the given values, we get \(z \approx 9.67\).
Step 5 :The p-value is the probability of observing a sample proportion as extreme as 0.92 or more extreme, given that the null hypothesis is true. In this case, the p-value is approximately 0.
Step 6 :If the p-value is less than the significance level (usually 0.05), we reject the null hypothesis. Here, since the p-value is 0, which is less than 0.05, we reject the null hypothesis.
Step 7 :Therefore, we conclude that more than \(\frac{4}{5}\) of adults rate themselves as above average drivers.
Step 8 :The final answer is the test statistic and the p-value, which are approximately 9.67 and 0 respectively. So, the final answer is \(\boxed{9.67, 0}\).