Analysing results

Determine the winner with statistical confidence. Document what you learned. Roll out winning variants and feed insights into the next test.

Introduction

Reading the results

Deciding when to call it

Implementing and documenting

Know when to stop, iterate, or extend tests

Conclusion

Related tools

VWO

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VWO

VWO provides A/B testing, personalisation, and behaviour analytics to optimise website conversion rates through data-driven experimentation.

Hotjar

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Hotjar captures user behaviour through heatmaps, session recordings, and feedback polls to understand how visitors use your website.

Microsoft Clarity

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Microsoft Clarity provides free session recordings, heatmaps, and user behaviour analytics without traffic limits or time restrictions.

Notion

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Notion

Flexible workspace for docs, wikis, and lightweight databases ideal when you need custom systems without heavy project management overhead.

Related wiki articles

A/B testing

Compare two versions of a page, email, or feature to determine which performs better using statistical methods that isolate the impact of specific changes.

Control group

Maintain an unchanged version in experiments to isolate the impact of your changes and prove causation rather than correlation with external factors.

Sample size

Calculate how many users you need in experiments to detect meaningful differences and avoid declaring winners prematurely based on insufficient data.

Statistical significance

Determine whether experiment results reflect real differences or random chance to avoid making expensive decisions based on noise instead of signal.

Lead capture rate

The percentage of engaged website visitors who submit their contact information and become leads.

Further reading

Experimentation

Experimentation

Determine the winner with statistical confidence. Document what you learned. Roll out winning variants and feed insights into the next test.