Test hypothesis
A test hypothesis is a statement formulated to predict the outcome of a specific change on a website or application as part of an optimization process.
A test hypothesis is a statement formulated to predict the outcome of a specific change on a website or application as part of an optimization process. Derived from prior data analysis, it aims to answer a specific question regarding the expected impact on a predefined KPI.
The typical formulation of a hypothesis is: "If we change [element], then we will see [desired outcome]". This hypothesis then guides the design and execution of the A/B test or multivariate test, and will be validated or refuted by a statistical significance test, whether Bayesian or frequentist.
This approach helps structure the optimization process methodically, ensuring that tests are based on solid observations and analyses.
By validating or refuting the hypothesis, companies can gain valuable insights into the effectiveness of the changes made, which helps them make informed decisions to improve the user experience and achieve their business goals.
Autres termes : A/B testing
View the full glossary- A/B TestingA/B(n) testing is a method that compares two or more versions of a web page, an application, an algorithm, advertising banners, etc., to determine which performs best.A/B testing
- AA TestA type of test where two groups receive the same experience to ensure that the testing system is reliable and that observed differences are not due to chance.A/B testing
- Client-side testing (A/B test)Client-side testing modifies the content visible to the user via scripts executed in the browser.A/B testing
- DatalayerA data structure used to transmit information between a website and a tag management tool.A/B testing
- Feature FlaggingA technique for enabling or disabling features remotely without deploying new code.A/B testing
- Feature RolloutThe progressive rollout of a new feature to a subset of users before a full launch.A/B testing