Flagship 2024 – Day 2 is live! Click here to register and watch now.



A t-test is a type of hypothesis test which assumes the test statistic follows the t-distribution. It is determined if there is a statistically significant difference between two groups.

Technical Definition

The t-test is the hypothesis test of the t-distribution. The t-distribution is a particular kind of probability distribution, similar to the normal distribution but the variance is estimated rather than known. There are various different types of t-tests; any hypothesis test which relies on the assumption that the parameter of interest follows a t-distribution falls under the t-test family. A t-test results in a t-score, which can then be translated to a p-value for easier interpretation and to determine statistical significance.

Different Types of T-Tests

There are different versions of the t-test that can be used in different scenarios, the three main types are:

Independent Samples T-Test

This is the type most commonly used in online experimentation. It compares the means for two independent groups. For example, when you randomly assign all visitors to a website into one of two groups, you are creating two separate samples of visitors who are independent from each other. The independent samples t-test can be used to test for differences between the average behavior of users in those two groups.

Paired Samples T-Test

This type of test is used for paired data, when each measurement in a sample is paired with a measurement from the other sample. For example in a repeated-measures design, each pair may contain measurements for the same unit before and after a treatment, or in a matched-pairs design each unit may be matched with a similar unit from another sample.

One Sample T-Test

The one-sample t-test can be used to determine whether the mean of a single sample differs from a particular value. For example, it could be used to determine whether the average exam score for a class of students differs from a particular target.

Want to Dive Deeper?

We have a lot to explore that can help you understand feature flags. Learn more about benefits, use cases, and real world applications that you can try.

Create Impact With Everything You Build

We’re excited to accompany you on your journey as you build faster, release safer, and launch impactful products.

Want to see how Split can measure impact and reduce release risk? 

Book a demo