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Use the T-test to analyze your Amplitude Experiment's results

It can be difficult to understand the benefits of running one statistical test over another, such as sequential testing versus the T-test.

A T-test is the **comparison of means** amongst two populations of data to determine if the difference is statistically significant. Amplitude computing uses the Welch's T-test, which comes with a few assumptions about your dataset:

- The Central Limit Theorem applies to the metric.
- Both populations do not share the same variance.
- You don't run the T-test until you've reached the sample size specified by the duration estimator.

T-tests can be completed as either **two-sided** (which looks for **any** change in the metric, in either direction) or **one-sided** (which looks for an increase **or** a decrease, but not both). For a two-sided test, a statistically significant increase or decrease **is not** explicitly stated, while for a one-sided test, it **is**. (If you select *Increase*, the upper confidence interval bound is positive infinity; for *Decrease*, the lower confidence interval bound is negative infinity.)

If you have yet to run your experiment or your sample size is large enough, you should use sequential testing instead of running a T-test. Read more about the difference in testing options in this blog.

You can access the T-test settings from the *Plan* tab in Amplitude Experiment. The settings will depend on the type of T-test that you'd like to run (one-sided or two-sided) and the direction you'd like the metric to move in (increase for up or decrease for down). To set your T-test's settings:

- Under your
*Primary Metric*, select*Increase*or*Decrease*.

- Under
*Statistical Settings,*choose*T-test*as the*Test Type*, choose*1-sided*or*2-sided*based on the type of T-test you'd like to run. For example, if you want to do a two-sided T-test looking for an increase, select*Increase*in the primary metric and*2-sided T-test*in statistical settings. - Enter the number of users needed under
*Samples Per Variant Needed*. If you're unsure of the sample size to enter in*Samples Per Variant*, use Amplitude's duration estimator. To learn more, see our Help Center article on planning experiments with the help of the duration estimator.

The T-test works by first computing the sample size you'll need before you can control for a specific false positive and false negative rate. Analyzing your data before reaching the sample size threshold will increase your error rates. See this article for more explanation on how peeking can interrupt your experiment process.

- Lastly, click
*Save*to change the statistical settings to T-test.

The *Analyze* tab now shows the T-test results of your experimental data. Read more about interpreting test results in our article on Amplitude's Experiment Results chart.

You'll need to reach a minimum sample size before you run a T-test. The *Analyze* tab of the T-test will warn you if your data set is too small.

You can find more information on your sample size requirements in the *Cumulative Exposure* graph and its corresponding table. The graph shows a constant, dotted line named *Sample Size Target*, which is the total number of users per variant needed. The table next to the graph highlights the *Exposure Remaining*, which is the number of users needed by each variant. This information can confirm not only the number of users needed before running the T-test, but also provide an estimate of the time you'll need to complete the experiment before using a T-test to interpret your results.

Unfortunately, reaching the needed sample size does not guarantee your results will be statistically significant. For example, if your lift is smaller than the MDE, then your results often will not be statistically significant.

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May 7th, 2024

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