Read time: 2 minutes
Last edited: Oct 17, 2023
This topic explains how to interpret an experiment's results and apply its findings to your product.
The data an experiment has collected is represented in a Results tab. The Results tab provides information about each variation's performance in the experiment, how it compares to the other variations, and which variations are likely to be best out of all the tested options. Understanding how to read this tab can help you make informed decisions about when to edit, stop, or choose a winning variation for your experiments.
Here is an example Results tab for a feature change experiment:
This table explains the different sections of the Results tab and what you can use them for:
|Example (click to enlarge)
|Attribute filters and the traffic count table help you examine experiment results for certain cross-sections of your experiment audience. To learn more, read Slicing experiment results.
|The sample size estimator helps you decide how long to run the experiment for. To learn more, read Experiment size and run time.
|A feature change experiment's probability chart and results table help you understand why the winning variation has the highest probability to be best. To learn more, read Winning variations for feature change experiments.
|A funnel optimization experiment's probability report tab displays the winning variation for each step in the experiment funnel as well as the winning variation overall. To learn more, read Winning variations for funnel optimization experiments.
If you're using Data Export, you can find experiment data in your Data Export destinations to further analyze it using third-party tools of your own.
To learn more, read Data Export.