To perform the two-sample t-test using RStudio, we can follow these steps:

1. Load the data into RStudio and assign it to a variable (e.g. 'gas_data').
2. Split the data into two groups based on the service station (e.g. 'station1' and 'station2').
3. Calculate the mean and standard deviation of the sales for each group.
4. Use the t.test() function to perform the two-sample t-test, specifying the two groups and assuming unequal variances (i.e. var.equal = FALSE).

Here's the R code to perform these steps:

Step 1: Load the data

gas_data <- read.csv('gas_sales.csv')

Step 2: Split the data into two groups

station1 <- gas_data[gas_data$Station == 'Station 1',] station2 <- gas_data[gas_data$Station == 'Station 2',]

Step 3: Calculate the mean and standard deviation of sales for each group

mean1 <- mean(station1$Sales) sd1 <- sd(station1$Sales) mean2 <- mean(station2$Sales) sd2 <- sd(station2$Sales)

Step 4: Perform the two-sample t-test

t.test(station1$Sales, station2$Sales, var.equal = FALSE)

Assuming there are no confounding variables, the results of the t-test indicate whether there is a significant difference in the mean sales between the two service stations. The output of the t.test() function includes the t-statistic, degrees of freedom, and p-value. The p-value tells us the probability of observing a difference in means as extreme as the one we calculated, assuming there is no true difference between the populations. If the p-value is less than our chosen significance level (e.g. 0.05), we can reject the null hypothesis that the means are equal and conclude that there is a significant difference between the two service stations.