A fast-food company advertises that the pre-cooked weight for its half- pound burgers is, on average, 0.5 lbs. Alvaro is in charge of a quality control test of \( H_{0}: \mu=0.5 \) lbs versus \( H_{\mathrm{a}}: \mu \neq 0.5 \mathrm{lbs} \), where \( \mu \) is the mean weight of all burgers in a batch. Alvaro took a random sample of 30 burgers from a batch and found a mean weight of 0.49 lbs and a sample standard deviation of 0.04 lbs . Based on these results, he calculated a test statistic of \( t \approx-1.37 \) and a P-value of approximately 0.181 . Assuming the conditions for inference were met, what is an appropriate conclusion at the \( \alpha=0.05 \) significance level? Choose 1 answer: (A) Reject \( H_{0} \). This is strong evidence that the mean weight is different than 0.5 lbs. (B) Reject \( H_{0} \). This isn't enough evidence to conclude that the mean weight is different than 0.5 lbs. (C) Fail to reject \( H_{0} \). This is strong evidence that the mean weight is different than 0.5 lbs. (D) Fail to reject \( H_{0} \). This isn't enough evidence to conclude that the mean weight is different than 0.5 lbs.
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Because the P-value of approximately 0.181 is greater than the significance level of \( \alpha=0.05 \), we do not have sufficient evidence to reject the null hypothesis \( H_{0}: \mu=0.5 \) lbs. Thus, the appropriate conclusion is to fail to reject \( H_{0} \), indicating there isn't enough evidence to support the claim that the mean weight of the burgers is different from 0.5 lbs. Therefore, the correct answer is (D) Fail to reject \( H_{0} \). This isn't enough evidence to conclude that the mean weight is different than 0.5 lbs. On a lighter note, this situation mirrors our everyday experiences, like when you order a burger and it doesn’t quite meet your expectations! Quality control is essential to ensure that we get that hearty, half-pound bite every time. You might just want to double-check your burger before taking that satisfying first bite!
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