![]() For post-test, your CSV needs to have at least 2 columns of order values for the duration of your experiment.Ī Type I error is simply a false positive, for example when the test shows that a variant is a winner but in reality, it’s not. In its post-test mode, it does post hoc results analysis, using power calculations it can predict dynamically how advanced the test currently is.īoth modes support revenue metrics, for pre-test you will need a CSV file with one column of a week’s worth of order values to run MDE calculations for it. You can then choose a compromise depending on how long you are ready to wait and how small a Lift you wouldn’t want to miss. ![]() For instance, given 5000 visitors per week with 250 of those converting, we can observe at least those Minimum Detectable Effects in a statistically significant fashion over a period of weeks. In the pre-test mode, it does test planning by calculating the minimum detectable effects that would work for up to 12 weeks, given the current traffic and conversion values. It can handle both pre-test and post-test scenarios. This calculator answers questions about the three most-used metrics in A/B testing, conversion rate, average order value, and average revenue per visitor. If it is inferior to or less than the “risk” you are willing to accept - the standardized value of this being 5% (the significance level) - then you can be reasonably confident of your test (at your chosen level of confidence, in that case, 100% - 5% significance level = 95% confidence level). P-value gives you the probability that you have seen a 2 percentage point increase in the “Add to Cart” KPI for your variation (Page B) or a more extreme result if the null hypothesis (that there is really no change) was true. Right? (PS: Don’t make assumptions in marketing or optimization… being data-driven is the way to go). No! Because all the visitors to your website have not seen page B and you can’t make assumptions about their preferences simply from observing the behavior of a much smaller sample size. So should you just add 3D images to your product pages across the site? The conversion rate in terms of “Add to Cart” is 7% for A and 9% for B. The second one (we’ll call it B, or the variation) has them. The first one (we’ll call it A, or the control) does not have 3D product images. Let us assume you’re running an A/B test on two landing pages selling the same product. It signifies that our null hypothesis is highly unlikely to be true, hence it proves that we have the effects we are observing are not due to random chance. The statistical significance in A/B testing is when the p-value becomes inferior to our significance threshold.
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