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Results interpretation

Interpreting A/B test results is a systematic process that goes beyond just checking if your new version won. Here's how to analyze your results effectively:

  • Check statistical validity: Start by confirming your test reached statistical significance (95% confidence) and ran for a full business cycle. A test showing 90% confidence after just two days isn't reliable enough for decision-making. Look at your sample size — did you get enough traffic in both the control and test groups?
  • Examine primary metrics: Look at your main success metric from your hypothesis. For example, if you predicted your new checkout design would increase conversion rate by 15%, check the actual numbers. Did version B achieve a 14% increase? That's close enough to call it successful. Did it only achieve 2%? That might not be worth the implementation effort.
  • Review secondary impacts: Sometimes improvements in one area create problems elsewhere, so check related metrics that might be affected. For a checkout test, this could be average order value, support ticket volume, time to complete checkout, and cart abandonment rate.

Pro Tip: Document both positive and negative impacts of each test — they help inform future test hypotheses and prevent repeating mistakes.

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