This article helps you:
See your experimental results
Understand and interpret those results
You’ve designed your experiment, rolled it out to your users, and given them enough time to interact with your new variants. Now, it’s time to check if your hypothesis was correct.
In the Activity tab, you’ll be able to tell at a glance whether your experiment has yielded statistically significant results as well as what those results actually are. Experiment takes the information you gave it during the design and rollout phases and enters them in for you automatically limiting repetition of effort. It breaks the results out by variant and provides you with a convenient, detailed tabular breakdown.
Depending on your experiment, not all cards described below may be visible.
On the Filter card, set criteria that updates the analysis on the page. Filter your experiment results with the following:
The date filter defaults to your experiment's start and end dates. Adjust the range to scope experiment results to those specific dates.
The segment filter enables you to select predefined segments, or create one ad-hoc. Predefined segments include:
The Testers, Exclude Testers, and variant jumpers segments are available on feature experiments that use Remote evaluation.
The Exclude users who variant jumped segment and and exclude testers and variant jumpers segment are available on experiment types other than multi-armed bandit.
These segments update in real-time.
Click the segment icon and then click Create Segment to open the Segment builder and then define a new segment however you want. Segments you create in one experiment are available across all other experiments and appear in the All Saved Segments category.
Filter your experiment results based on user properties. For example, create a filter that excludes users from a specific country or geographic region, or users that have a specific account type on your platform.
Click Add filter to build a property filter.
Data Quality is available to organizations with access to Experiment who have recommendations enabled.
Amplitude doesn't generate p-values or confidence intervals for experiments using binary metrics (for example, unique conversions) until each variant has 100 users and 25 conversions. Experiments using non-binary metrics need only to reach 100 users per variant.
When you expand a category, or click Guide, the Data Quality Guide opens in a side panel where you can address or dismiss issues
Summary is available to organizations with access to Experiment who have recommendations enabled.
The Summary card describes your experiment's hypothesis and lets you know if it's reached statistical significance.
Amplitude considers an experiment to be statistically significant (stat sig) when Amplitude can confidently say that the results are unlikely to have occurred due to random chance. More technically, it’s when Amplitude rejects the null hypothesis. That may sound subjective, but it’s grounded in statistics. Statistical significance relies on a variant’s p-value, which is a value that represents the likelihood that your results occurred by chance. A lower p-value means your results are probably not random, and there's evidence to support your hypothesis. If this value drops below a threshold, Amplitude considers the experiment to be statistically significant.
The Summary card displays a badge labeled Significant if the experiment reached statistical significance, and a badge labeled Not Significant if it didn't. This card can display several badges at the same time:
At the top of the Analysis card is an overview that explains how your experiment performed, broken down by metric and variant. Below that, a collection of experiment results charts, which you can analyze by metric, display information about:
For more information, review Dig deeper into experimentation data with Experiment Results
The Experiment Results chart on the Activity tab responds to the selections you make in the Filter card.
Click Open in Chart to open a copy of the Experiment Results in a new chart.
If you are running an A/B/n test, Amplitude Experiment displays the confidence interval / p-value for the control against each treatment individually. To view the comparison between two non-control treatments, either change the control variant or open the test in Analytics and create a chart using the two treatments you're interested in.
If needed, adjust the experiment’s confidence level. The default is 95%. You can also choose between a sequential test and a T-test.
Lowering your experiment’s confidence level makes it more likely that your experiment achieves statistical significance. However, this increases the likelihood of a false positive.
Group-bys in Experiment charts is a Beta feature, and may result in slower query performance. For more information, review Limitations.
Oftentimes, you run an experiment and want to know if the experiment effected different users differently. In other words, if there are heterogeneous treatment effects or not. One way to do this is to filter for Platform = iOS and then Platform = Android and then Platform = Web. Grouping results by Platform achieves the same result, with fewer clicks. For more information, review Group-bys: How Amplitude prunes and orders chart results for more information.
Group-by settings on the dashboard are temporary. Refreshing the dashboard resets any group-bys you define.
The Diagnostics card provides information about how your experiment is delivering. It shows charts about:
For more control, open any of these charts in the Analysis Chart modality.
You can receive notifications about your experiments and have them sent to either a dedicated Slack channel or to a unique webhook. Go to Integrate Slack and then Experiment Notifications to set up these notification alerts.
You can set up a notification for the following events:
Amplitude Experiment sends a notification to the editors of the experiment.
It’s important to remember that no experiment is a failure. Even if you didn’t get the results you were hoping for, you can still learn something from the process. Use your results as a starting point to asking questions about the changes you made, the outcomes you saw, what your customers expect from your product, and how you can deliver on those expectations.
In general, the next step should be deciding whether to conduct another experiment that supports your hypothesis to gather more evidence, or to go ahead and implement the variant that delivered the best results. You can also export your experiment to the Experiment Analysis in Amplitude Analytics and conduct a deeper dive there, where you can segment your users there and hopefully generate more useful insights.
September 22nd, 2025
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