# Lift and confidence - A4T FAQ

## Can I perform offline calculations for A4T? section_55B5B750E17D414CAECBEECE27B15D81

You can perform offline calculations for A4T, but it requires a step with data exports in Analytics. For more information, see Statistical calculations in A/Bn tests.

## How is lift calculated? section_8CAE788EED5646C4B1D64A0D22070734

Lift is the percent difference between your control page results and a successful test variant.

## How is confidence calculated? section_97DB24D833E742988318CA65DA65DAD9

The confidence level is a probability, expressed as a percentage, that is equal to `1 - p-value`, where the `p-value` is computed from a t-test. See Statistical calculations in A/Bn tests.

## Why can’t I see lift and confidence on calculated metrics? lift-confidence

Calculated metrics are not currently supported in lift and confidence functions. Analytics calculates metrics at an aggregate-level, rather than at a visitor-level. Confidence, in particular, is a visitor-level calculation.

Non-calculated (standard) events are supported in lift and confidence. They become the numerator in the lift function; the numerator cannot be a calculation itself. The denominator is the normalizing metrics (impressions, visits, or visitors). Some examples of standard events include orders, revenue, activity conversions, custom events 1-1000, and so on. Common optimization metrics, such as conversation rate (Orders/Visitor) and RPV (Revenue/Visitor) are supported in lift and confidence.

Examples of unsupported metrics or use cases include:

• Average Order Value (Revenue/Order, per Visitor). AOV is not supported because the numerator is a calculated metric. Instead, the recommendation is to consider the two influencing metrics of AOV - Revenue Per Visitors and Conversion Rate.
• Calculated metrics that are the sum of standard events. For example, you can track ten different lead forms into ten separate events, and then add them together to get total lead submissions. A recommended method to track these events is to implement a single lead submission event in Analytics and then use an eVar to collect the type of lead form. Using this method requires fewer variables and ensures that you can use the single lead submission metric in lift and confidence functions.