QuestionA large data set includes samples of body temperatures. Analyzing one sample of body temperatures results in $n=103, \bar{x}=98.19^{\circ} \mathrm{F}$, and $\mathrm{s}=0.617^{\circ} \mathrm{F}$. It is commonly believed that humans have a mean body temperature of $98.6^{\circ} \mathrm{F}$. If the analysis is repeated with a different sample and it is found that for 100 randomly generated samples, 38 of these generated samples have a mean that is as extreme as the mean of the actual sample, what should be concluded about the assumed mean of $98.6^{\circ} \mathrm{F}$ ? (Assume that an event is significant if it has a probability of 0.05 or less.)
Since 38 of the 100 samples have means that are as much as the sample mean, then that sample mean $\square$ so there $\square$ strong evidence against the assumed mean of $98.6^{\circ} \mathrm{F}$. It appears the population mean $\square$ $98.6^{\circ} \mathrm{F}$