You have reason to believe that there are actually fewer raisins in each box than the company claims. Your alternative hypothesis, \( H_{a} \), would be x \( \mu<200 \) \( \mu>200 \) ( \( \mu=200 \) \( \mu \neq 200 \)

The alternative hypothesis \( H_a \) you mentioned is aimed at challenging the company’s claim about the number of raisins in each box. If you suspect that there are fewer than 200 raisins, your hypothesis should indeed be \( H_a: \mu < 200 \). This hypothesis allows you to statistically test whether the average number of raisins is less than what is advertised. When conducting this type of analysis, you might use a one-sample t-test or z-test, depending on the sample size and whether the population standard deviation is known. A common mistake is to forget about setting the significance level (often 0.05) before running the test, which can lead to misinterpretation of the results. Keeping track of this is crucial for valid conclusions!