More feature flags related terms

Confidence interval


In the context of A/B testing, a confidence interval is a range of values around the observed difference in performance metrics between two variations (A and B) of a website, app, or marketing campaign. It provides a measure of uncertainty about the true difference in performance between the variations and helps assess the reliability of the observed results.


Confidence intervals in A/B testing serve several important purposes:


The confidence interval for an A/B test typically involves calculating the confidence interval around the difference in conversion rates, click-through rates, or other relevant metrics between Variation A and Variation B. It is calculated based on the observed data from both variations and is influenced by factors such as sample size and statistical significance level.


A confidence interval in A/B testing provides a range of values within which we are confident that the true difference in performance between variations lies. For example, a 95% confidence interval for the difference in conversion rates between Variation A and Variation B of [-0.02, 0.05] means that we are 95% confident that the true difference in conversion rates falls within this range. If the confidence interval includes zero, it suggests that there may not be a statistically significant difference between the variations.

Use Cases



In A/B testing, confidence intervals play a crucial role in assessing the reliability and significance of observed differences in performance metrics between variations. By providing a range of plausible values for the true difference in performance, confidence intervals help analysts interpret results, make informed decisions, and communicate the uncertainty associated with A/B test outcomes to stakeholders.

Bucket testing

Bucket testing, also called A/B testing, compares two versions of a webpage or app to see which performs better, aiding data-driven decisions for enhancing user experience and achieving business goals through iterative improvements.

Learn about Bucket testing

Multivariate testing

Testing multiple variables in a controlled environment to determine which combination yields the best outcomes.

Learn about Multivariate testing

User segmentation

Dividing users into groups based on behavior or attributes for targeted feature releases.

Learn about User segmentation

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