Skip to content
~/reflective_data

analytics-dictionary / term / experimentation

CUPED

CUPED reduces variance in experiments by using pre-experiment data about each user, so tests reach conclusions faster.

Written by Reflective Data experts · updated Oct 5, 2026

It adjusts each user metric using their behaviour before the test. Because much of the noise between users is stable over time, removing it makes real differences easier to see.

It is widely used in mature experimentation platforms and can cut the required sample size noticeably.

related

A/B testMinimum detectable effect (MDE)Statistical power

// how we can help

We set up, audit and run experimentation programs on any tool. A/B testing services → · Audits

more in experimentation

convert.md

Convert

Convert is an A/B testing platform known for privacy features and fast, lightweight testing.
personalization.md

Personalization

Personalization tailors content or offers to individual users or segments, based on their behaviour or attributes.
exposure-logging.md

Exposure logging

Exposure logging records the moment a user is actually shown a variant, so analysis only includes…
win-rate.md

Win rate

Win rate is the percentage of experiments that produce a significant improvement.
type-1-and-type-2-errors.md

Type I and type II errors

A type I error is a false positive, declaring an effect that is not real; a…
adobe-target.md

Adobe Target

Adobe Target is Adobe testing and personalisation tool, part of Adobe Experience Cloud.

← full analytics dictionary