A meteorologist claims that the average daily high temperature in Oklahoma City is \( 90^{\circ} \). Let \( \mu \) represent the average daily high temperature, in \( { }^{\circ} \mathrm{F} \), in Oklahoma City during the summer. What is the null hypothesis, \( H_{0} \), for this situation? \( \mu<90 \) \( \quad \mu=90 \) \( \mu>90 \) \( \mu \geq 90 \)

The null hypothesis \( H_{0} \) for this situation is \( \mu = 90 \). This means that the meteorologist believes there is no difference between the average daily high temperature and the claimed average of \( 90^{\circ} \). Essentially, the null hypothesis serves as a starting point for statistical testing, asserting that any observed variations in temperature are due to chance rather than a significant change in temperature. When conducting a hypothesis test, the idea is to gather evidence against the null hypothesis. If the data collected show that the average is significantly different from \( 90^{\circ} \) (either higher or lower), that could lead the researchers to reject the null hypothesis in favor of an alternative hypothesis. This process is a cornerstone of inferential statistics, allowing scientists and statisticians to make educated conclusions based on sample data!