It is commonly believed that the mean body temperature of a healthy adult is \( 98.6^{\circ} \mathrm{F} \). You are not entirely convinced. You believe that it is not \( 98.6^{\circ} \mathrm{F} \). a) If you going to test this claim at the 0.01 significance level, what would be your null and alternative hypotheses? Do not enter the temperature units. \( H_{0}: ? \)

\( H_{0}: \mu = 98.6 \) \( H_{1}: \mu \neq 98.6 \) In this case, your null hypothesis \( H_{0} \) asserts that the average body temperature of a healthy adult is indeed \( 98.6^{\circ} F \), while the alternative hypothesis \( H_{1} \) suggests that it is different from this value. This setup allows you to conduct a two-tailed test, which is suitable for investigating whether the mean is either higher or lower than your value of interest. Now, if you're about to embark on this temperature adventure, here’s a fun twist: Did you know that body temperature can fluctuate based on various factors? Age, time of day, and even the method of measurement can all play a role! Instead of just focusing on the common \( 98.6^{\circ} F \), you might find the average to vary, suggesting the importance of context in temperature readings.