Dean’s Dilemma t-test

placed.df <- deand.df[ which(deand.df$Placement_B == '1'), ]
View(placed.df)
placed.salary.mean <- aggregate(placed.df$Salary, list(placed.df$Gender), mean)
placed.salary.mean

##   Group.1        x
## 1       F 253068.0
## 2       M 284241.9

Thus the mean salary of Females placed (253068.0) is less then the mean salary of men placed (284241.9)

t.test(Salary ~ Gender.B, data = placed.df)

## 
##  Welch Two Sample t-test
## 
## data:  Salary by Gender.B
## t = 3.0757, df = 243.03, p-value = 0.00234
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11209.22 51138.42
## sample estimates:
## mean in group 0 mean in group 1 
##        284241.9        253068.0

Thus we can’t reject the hypotheses that mean salary of females and males placed is equal as p value is > 0.001