This article will help you:
 View your Funnel Analysis charts in terms of either improvement over baseline or statistical significance
NOTE: For best practices, including tips on instrumentation, please take a look at our How to Analyze A/B Tests Results in Amplitude article.
In Amplitude, A/B testing lets you compare the funnel conversion performance of two or more user segments against each other. You can view results in terms of improvement—which describes the performance of a segment compared to the baseline—or in terms of statistical significance, which will show you the probability of observing a difference as extreme as what you saw, assuming the control and treatment have the same mean.
Feature availability
This feature is available to users on Growth and Enterprise plans only. See our pricing page for more details.
Amplitude will, by default, use the first segment added to the funnel analysis as the baseline, but you can change this in the Baseline segment dropdown menu.
A/B Test  Improvement
This chart will display the conversion rate for each segment across all steps in your funnel. You can have more than just one variant in an A/B test, but you can only have one baseline.
A/B Test  Significance
On this chart, a high value for a variant suggests it will convert better than the baseline, while a low value suggests it won't.
If these results include:
 a sample size above 30 for both variants being compared
 sample size * conversion rate >= 5 and sample size * (1conversion rate) >= 5 for both variants
 a significance of 95% or greater
then Amplitude will consider the results significant.
Get more details on how Amplitude calculates statistical significance.
Understand the breakdown table
The data table below the chart will give you a breakdown of the data. As with all data tables in Amplitude, you can export the data as a CSV file. Here are the columns included:
 Count: The number of users or groups that entered the funnel.
 Converted: The number of users or groups that completed all the steps in the funnel with all conditions met.
 % Conversion: The number of converted users or groups, divided by the number of users or groups that entered the funnel.
 % Improvement over Baseline: This is calculated by the equation (% conversion for that variant  % conversion for the baseline) / (% conversion for the baseline). The percentage in the data table will be green when the value is a positive number.

Significance: This is the likelihood that the performance displayed for each test variant is actually different from zero, and not due to random fluctuations in the data. The higher this value is, the more confident you can be in your results. More formally, this can be described as 1  pvalue.