Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean? Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males ............................................................. Females
H0: Mean salary = 45 .......................... H0: Mean salary = 45
Ha: Mean salary = / = 45 .................. Ha: mean salary = / = 45
While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances, having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.

2. (a) Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place B43 in Outcome range box.
P-value is:
Is P-value Reject or do not reject Ho:
If the null hypothesis was rejected, what is the effect size value?
Meaning of effect size measure:
b. Since the one and two tail t-test results provided different outcomes, which is the proper/correct approach to comparing salary equality? Why?
3. Based on our sample data set, can the male and female compass in the population be equal to each other? (Another 2-sample t-test.)
Ho:
Ha:
Statistical test to use:
Place B75 in Outcome range box.
What is the p-value?
Is P-value Reject or do not reject Ho:
If the null hypothesis was rejected, what is the effect size value?
Meaning of effect size measure:

Male Female Mean 52 45 Mean 38 45 Variance 316 Variance 334.667 Observations 25 25 Observations 25 25 Hypothesized Mean Hypothesized Mean Difference Difference df 24 df 24 t Stat 1.96890383 t Stat -1.9132 P(T