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This problem involves testing the relationship between salary and gender using a hypothesis test for independence. The researcher is trying to determine whether salary and gender are independent or dependent variables.

The null and alternative hypotheses are typically framed as follows for a chi-square test of independence:

- **Null Hypothesis (H₀):** Salary and gender are independent (i.e., there is no association between salary and gender).
- **Alternative Hypothesis (Hₐ):** Salary and gender are dependent (i.e., there is an association between salary and gender).

So, the correct answer is:
- \( H_0: \) Salary and gender are independent.
- \( H_a: \) Salary and gender are dependent.

Do you have any further questions, or would you like more details on the next steps of the test?

Here are 5 related questions:
1. How do you calculate the expected frequencies in a chi-square test of independence?
2. What are the assumptions for using the chi-square test of independence?
3. How do you interpret the p-value in a chi-square test?
4. What is the formula for the chi-square statistic?
5. What are degrees of freedom, and how do you calculate them in this context?

**Tip:** The chi-square test compares the observed frequencies in the table with the expected frequencies to determine if there is a statistically significant association between two categorical variables.

A researcher wants to study the relationship between salary and gender. She randomly selects 318 individuals and determines their salary and gender. Can the researcher conclude that salary and gender are dependent?

Explore the relationship between salary and gender using a chi-square test of independence. This example problem involves a sample of 318 individuals, with hypotheses testing whether salary and gender are independent. Includes step-by-step explanation and key statistical formulas.

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