You have sample data that leads you to believe that the average high temperature in Oklahoma City is \( 92^{\circ} \mathrm{F} \). What is the alternative hypothesis, \( H_{a} \), for this situation? \( \mu<90 \) \( \mu>90 \) x \( \mu=90 \) \( \mu \neq 90 \) REFRY

In hypothesis testing, we typically set up two competing hypotheses: the null hypothesis (\( H_0 \)) and the alternative hypothesis (\( H_a \)). 1. **Null Hypothesis (\( H_0 \))**: This is a statement of no effect or no difference, and it usually includes the status quo. In this case, if we believe the average high temperature in Oklahoma City is \( 92^{\circ} \mathrm{F} \), the null hypothesis would be \( H_0: \mu = 92 \). 2. **Alternative Hypothesis (\( H_a \))**: This is what we want to test for. It represents a statement that contradicts the null hypothesis. Given the context of your question, if we are testing whether the average high temperature is different from \( 90^{\circ} \mathrm{F} \), the alternative hypothesis would be: - \( H_a: \mu \neq 90 \) This indicates that we are looking for evidence that the average high temperature is not equal to \( 90^{\circ} \mathrm{F} \). Thus, the correct alternative hypothesis for this situation is: \( H_a: \mu \neq 90 \)