# Hypothesis Tests: 2 Sample Tests (A/B Tests)

Businesses are increasingly beginning to use data to drive decision making, and are often using hypothesis tests. Hypothesis tests are used to differentiate between a pair of potential solutions, or to understand the performance of systems before and after a certain change. We’ve already seen t-tests and how they’re used to ascribe a range to the variability inherent in any data set. We’ll now see the use of t-tests to compare different sets of data. In website optimization projects, these tests are also called A/B tests, because they compare two different alternative website designs, to determine how they perform against each other.

It is important to reiterate that in hypothesis testing, we’re looking for a significant difference and that we use the p-value in conjunction with the significance (%) to determine whether we want to reject some default hypothesis we’re evaluating with the data, or not. We do this by calculating a confidence interval, also called an interval estimate. Let’s look at a simple 2-sample t-test and understand how it works for two different samples of data.

## Simple 2-sample t-test

A 2-sample t-test has the default hypothesis that the two samples you’re testing come from the same population, and that you can’t really tell any difference between them. So, any variation you see in the data is purely random variation. The alternative hypothesis in this test, is of course, that it isn’t only random variation we’re seeing, and that these samples come from completely different populations altogether.

$H_o : \mu_1 = \mu_2 \newline H_a: \mu_1 \neq \mu_2$

We’ll generate two samples of data $x_1, x_2$ from two different normal distributions for the purpose of demonstration, since normality is a pre-requisite for using the 2-sample t-test. (In the absence of normality, we can use other estimators of central tendency such as the median, and the tests appropriate for estimating the median, such as the Moods-Median or Kruskall-Wallis test – which I’ll blog about another time). We also have to ensure that the standard deviations of the two samples of data we’re testing are comparable. I’ll also demonstrate how we can use a test for standard deviations to understand whether the samples have different variability. Naturally, when the samples have different standard deviations, tests for assessing similarities in their means may not be fully effective.

library(nortest)
#Generating two samples of data
#100 points of data each
#Same standard deviation
#Different values of mean (of sampling distribution)
x1<-x[,2]
x2<-x[,3]

#Setting the global value of significance
alpha = 0.1

#Histograms
hist(x1, col = rgb(0.1,0.5,0.1,0.25), xlim = c(7,15), ylim = c(0,15),breaks = seq(7,15,0.25), main = "Histogram of x1 and x2", xlab = "x1, x2")
abline(v=10, col = "orange")
hist(x2, col = rgb(0.5,0.1,0.5,0.25), xlim = c(7,15),breaks = seq(7,15,0.25), add = T)
abline(v=12, col = "purple")

#Running normality tests (just to be sure)

#F-test to compare two or more variances
v1<-var.test(x1,x2)

if(ad1$p.value>=alpha & ad2$p.value>=alpha){
if(v1$p.value>=alpha){ #Running a 2-sample t-test for (i in c(-2,-1,0,1,2)){ temp<-t.test(x=x1,y=x2,paired = FALSE,var.equal = TRUE,alternative = "two.sided",conf.level = 1-alpha, mu = i ) cat("Difference= ",i,"; p-value:",temp$p.value,"\n")
}

}
}

The first few lines in the code merely include the “nortest” package and invoke/generate the data sets we’re comparing. The nortest package contains the Anderson Darling Normality Test, which we have also covered in an earlier post. We can generate a histogram, to understand what $x_1$ and $x_2$ look like.

The overlapping histograms of $x_1$ and $x_2$ clearly indicate the difference in the central tendency, and the overlap is also visible. Subsequent code above covers an F-test. As explained earlier, equality of variances is a pre-requisite for the 2-sample t-test. Failing this would mean that we essentially have samples from two different populations, which have two different standard deviations.

Finally, if the conditions to run a 2-sample t-test are met, the t.test() command (which is present in the “stats” package, runs, and provides us a result. Let’s look closely at how the t.test command is constructed. The arguments contain $x_1$ and $x_2$, which are our two samples for comparison. We provide the argument “paired = FALSE”, because these are not before/after samples. They’re two independently generated samples of data. There are instances where you may want to conduct a paired t-test, though, depending on your situation. We’ve also specified the confidence level. Note how the code uses a global value of $\alpha$, or significance level.

Now that we’ve seen what the code does, let’s look at the results.

## Evaluating The Results

Two sample t-test results should be evaluated in a similar way to 1-sample t-tests. Our decision is dependent on the p-value we see in the result, and the confidence interval of the difference between sample means.

Observe how the difference estimate lies on the negative side of the number line. Difference is calculated from the populations means 10 and 12, so we can clearly understand why this estimate of difference is negative. The estimates for mean values of x and y (in this case, $x_1$ and $x_2$) are also given. Naturally, the p-value that’s in the result, when compared to our generous $\alpha$ of 0.1, is far lesser, and we can consider this to be a significant result (provided we have sufficient statistical power – and we’ll discuss this in another post). This indicates a significant difference between the two sets. If $x_1$ and $x_2$ were fuel efficiency figures for passenger vehicles, or bikes, we may actually be looking at better performance for $x_2$ when compared to $x_1$.

## Detecting a Specific Difference

Sometimes, you may want to evaluate a new product, and see if it performs at least x% better than the old product. For websites, for instance, you may be concerned with loading times. You may be concerned with code runtime, or with vehicle gas mileage, or vehicle durability, or some other aspect of performance. At times, the fortunes of entire companies depend on them producing faster, better products – that are known to be faster by at least some amount. Let’s see how a 2-sample t-test can be used to evaluate a minimum difference between two samples of data.

The same example above can be modified slightly, to test for a specific difference. The only real difference we have to make here, of course, is the value of $\Delta$ or difference. The t.test() command in R unfortunately isn’t very clear on this – it expects you to understand that you should use $\mu$ for this. Once you get used to it, however, this little detail is fine, and it delivers the expected result.

if(ad1$p.value >=alpha & ad2$p.value>=alpha){
if(v1$p.value>=alpha){ #Running a 2-sample t-test for (i in c(-2,-1,0,1,2)){ temp<-t.test(x=x1,y=x2,paired = FALSE,var.equal = TRUE,alternative = "two.sided",conf.level = 1-alpha, mu = i ) cat("Difference= ",i,"; p-value:",temp$p.value,"\n")
}
}
}


The code above prints out different p-values, for different tests. The data used in these tests is the same, but by virtue of the different differences we want to detect between these samples, the p-values are different. Observe the results below:

Since the data was generated from two distributions that have means of 10 and 12 respectively for $x_1$ and $x_2$, we know from intuition that the difference is -2, and we should start seeing results that indicate no difference between the expected and observed difference at this value in the test. Therefore, the p-values in this scenario will be greater than the significance value, $\alpha$.

For other scenarios – when $\Delta = -1, 0, 1, 2$, we see that the p-values are clearly far below the significance of $\alpha = 10%$.

What’s important to remember therefore, is that contrary to what many people may think, there is no one or best p-value for a given set of data. It depends on the factors we take into consideration during the test – such as the sample size, the confidence level we chose for our test, the resulting significance level, and, as illustrated here, the difference expected.

## Concluding Remarks

A 2-sample t-test is a great way for an organization to compare samples of data from different products, processes, and so on, and understand if one of them is performing significantly better than another. The test is strictly for data that fits the normality criteria, that also happen to have comparable standard deviations, and the results from it tend to be impacted heavily by the kind of hypothesis we use – for difference (which we explored here) and for one or two sided comparisons. We explored only the two sided comparisons here (and hence constructed a two sided confidence interval). When a business uses a 2-sample t-test, some of the arguments here, such as the values of confidence level, difference and so on, should be evaluated thoroughly. It is also important to bear in mind the impact of sample size. The smaller the difference we want to detect, the greater the sample sizes have to be. We’ll see more about this in another post, on power, difference and sample size.