This article will help you:
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 see if your hypothesis was correct.
In the Analysis panel, 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. Amplitude Experiment takes the information you gave it during the design and rollout phases and plugs them in for you automatically, so there’s no repetition of effort. It breaks the results out by variant, and provides you with a convenient, detailed tabular breakdown.
NOTE: This article continues directly from the article in our Help Center on rolling our your experiment. If you haven’t read that and followed the process it describes, do so before continuing here.
Amplitude will not generate statistical calculations for experiments using binary metrics (unique conversions) until each variant has 100 visitors and 25 conversions. Experiments using non-binary metrics need only to reach 100 visitors per variant.
To generate and view experimental results, follow these steps:
- In your experiment, open the Analyze tab. The tab includes two sections, Summary —which describes your experiment's hypothesis and notes whether it has or has not reached statistical significance—and Analysis.
An experiment is said to be statistically significant when we can confidently say that the results are highly unlikely to have occurred due to random chance. (More technically, it’s when we reject the null hypothesis.) That might sound pretty subjective—what does “highly unlikely” even look like, anyway?—but it’s grounded solidly in statistics. Stat sig relies on a variant’s p-value, which is the probability of observing the data we see, assuming there is no difference between the variant and the control. If this probability drops below a certain threshold (statisticians refer to this threshold as the alpha), then we consider our experiment to have achieved statistical significance.If your experiment failed to reach stat sig, click the Rollback button to stop showing this experiment's variants to users.
- At the top of the Analysis section is an overview of how your experiment performed, broken down by metric and variant. Below that is the experiment's exposure definition: how many variants were shown, what the primary metric was, and what the exposure event was. This is the event users will have to fire before being included in an experiment.
NOTE: The exposure event is not the same thing as the assignment event. If, for example, you’re running an experiment on your pricing page, a user might be evaluated on the home page for the experiment—but if they don’t visit the pricing page, they'll never actually be exposed to it. For that reason, this user should not be considered to be part of the experiment.
To learn more about exposure events, see this article in the Amplitude Developer Center.
The exposure definition's default state is collapsed. Expand it by clicking the Expand icon.
You can create a chart in Amplitude Analytics from this experiment by clicking Open in Analytics.
- In the Statistical Settings drop-down panel, set the experiment’s confidence level. The default is 95%. You can also choose between a sequential test and a T-test. Usually, sequential is the better choice.
NOTE: Lowering your experiment’s confidence level will make it more likely that your experiment achieves statistical significance, but the trade-off is that doing so increases the likelihood of a false positive.
- Set the time frame for your experiment analysis, either from the selection of pre-set durations, or by opening the date picker and choosing a custom date range.
The tables, graphs, and charts shown in the Analysis section are explained in depth in our Help Center articles on understanding the Experiment Analysis view and interpreting the cumulative exposures graph in Amplitude Experiment.
Congratulations! You’ve successfully designed, rolled out, and analyzed your 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—even if your test didn’t reach stat sig. Use your results as a springboard to asking hard questions about the changes you made, the outcomes you saw, what your customers expect from your product, and how you can deliver that.
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.