Does college major depend on whether you are an athlete or not? A survey asks a group of college students about their majors. It also groups the students by whether they are athletes or not. The data are recorded in the contingency table below, and a chi-square Test of Independence at the [tex]$5\%$[/tex] significance level is performed.

\begin{tabular}{|c|c|c|c|c|}
\hline & Arts & Humanities & Sciences & Row Total \\
\hline Athlete & [tex]$21.2$[/tex] & [tex]$20.6$[/tex] & [tex]$14.1$[/tex] & [tex]$56$[/tex] \\
\hline Non-Athlete & [tex]$14.8$[/tex] & [tex]$14.4$[/tex] & [tex]$9.9$[/tex] & [tex]$39$[/tex] \\
\hline Column Total & [tex]$36$[/tex] & [tex]$35$[/tex] & [tex]$24$[/tex] & [tex]$95$[/tex] \\
\hline
\end{tabular}

(a) The null and alternative hypotheses are:
- [tex]$H_0$[/tex]: The two variables are independent, so being an athlete does not affect college major.
- [tex]$H_a$[/tex]: The two variables are dependent, so being an athlete does affect college major.

(b) [tex]$\chi_0^2=3.5$[/tex]

(c) [tex]$\chi_{0.05}^2=5.991$[/tex]

(d) What conclusions can be made? Select all that apply.



Answer :

To determine whether college major depends on being an athlete or not, we perform a chi-square Test of Independence at the 5% significance level, given the data in the contingency table and the provided chi-square values. Here are the steps and conclusions:

### Step 1: State the hypotheses:
- Null Hypothesis ([tex]$H_0$[/tex]): The two variables (college major and being an athlete) are independent. That means being an athlete does not affect the choice of college major.
- Alternative Hypothesis ([tex]$H_a$[/tex]): The two variables (college major and being an athlete) are dependent. That means being an athlete does affect the choice of college major.

### Step 2: Calculate the chi-square statistic
- Given in the problem: [tex]\(\chi_0^2 = 3.5\)[/tex]

### Step 3: Determine the critical value
- The critical value for the chi-square distribution with the appropriate degrees of freedom at a significance level of 0.05 is given as:
[tex]\[ \chi^2_{0.05} = 5.991 \][/tex]

### Step 4: Compare the test statistic to the critical value
- Compare the observed chi-square value ([tex]\(\chi_0^2 = 3.5\)[/tex]) with the critical value ([tex]\(5.991\)[/tex]).

### Step 5: Make the decision
- If the observed chi-square value is less than the critical value ([tex]\( \chi_0^2 < \chi^2_{0.05} \)[/tex]), we fail to reject the null hypothesis.
- If the observed chi-square value is greater than the critical value ([tex]\( \chi_0^2 > \chi^2_{0.05} \)[/tex]), we reject the null hypothesis.

In our case:

[tex]\[ 3.5 < 5.991 \][/tex]

Therefore, we fail to reject the null hypothesis.

### Step 6: State the conclusion
- Since we fail to reject the null hypothesis, we conclude that there is not enough evidence to suggest that there is a dependency between being an athlete and college major. In other words, we cannot conclude that being an athlete has an effect on the choice of college major.

### Final Conclusion
The conclusions that can be made based on the chi-square Test of Independence result are:
- Fail to reject the null hypothesis. There is not enough evidence to suggest that the variables are dependent.

Thus, we conclude that there is not enough evidence to suggest that college major depends on whether a student is an athlete or not.