The following online test calculator for A/B testing has been sponsored by:
A/B Testing Significance Calculator: Supports Revenue Metrics Like AOV & ARPV
Should you even A/B test?
It is quite easy to adopt a “test everything” mindset - at least on paper.
But when it comes to actual execution, many sites do not meet the traffic requirements for statistically sound, iterative testing.
This doesn’t mean that they can’t be guided by qualitative and quantitative data.
It just means that when it comes to validating their hypotheses (the relationship between the dependent variable and the independent variable), they must take bigger and riskier bets and maybe even sacrifice causal relationships for the assurance that there is a good chance of seeing the “improvement” translate into real business impact.
This calculator by Convert helps plan your testing strategy – should you go for the big swings, or can you have a mapped-out, iterative testing game plan?
It lets you:
- Understand sample size requirements (how many visitors are needed to detect a particular effect, for specific power and confidence-risk - settings).
- Gauge testing cycles, given the sample size requirement.
- Consciously accept higher levels of risk (lower confidence) for tests where the impact of a false positive isn’t profoundly damaging to the business.
- Tweak power settings with MDEs (minimum detectable effect) to define your testing strategy.
We’ve also included revenue metrics like Average Order Value (AOV) and Average Revenue Per User (ARPU) so that Ecommerce stores can go beyond conversion rates and plan to optimize for direct impact on revenue.
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