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#Sequential testing cro how to

Think of an MDE in terms of medical testing. You should remember that this term was created before AB testing as we know it now. When calculating the sample size, you will need to specify the significance level, power and the desired relevant difference between the rates you would like to discover.Ī note on the MDE: I see some people struggle with the concept of MDE when it comes to AB testing. There are of course several available online calculators that you can you use as well. The formula for calculating the sample size is pretty complicated so better ask the statistician to do it.

#Sequential testing cro how to

How to calculate the sample size for an A/B test?įor no-math-scared readers, I will provide an example of such a calculation later in the post. To avoid type II errors, you set the power at 0.8 or 0.9 if possible when calculating your sample size, making sure that the sample size is large enough.To avoid type I errors, you specify a significance level when calculating the sample size.

You avoid both of these errors when calculating your sample size. These two situations are illustrated below: With type I errors, you might reject the hypothesis that should NOT be rejected concluding that there is a significant difference between the tested rates when in fact it isn’t. Type II errors occur when are not able to reject the hypothesis that should be rejected. Making a mistake in your analysis based on faulty data (point 3) will impact the decisions you make for the population This is how the statistics work: you draw conclusions from the population based on what you see for your sample. We use the sample conversion rate to draw conclusions about the population conversion rate. The sample conversion rate is the control conversion rate while conducting the test. The population conversion rate is the conversion rate for the control for all visitors that will come to the page. It is important to remember that there is a difference between the population conversion rates and the sample size conversion observed rates r. To prevent this problem from happening, you need to calculate the sample size of your experiment before conducting it. Because of the data, you are completely unaware of it. You are not able to detect a difference between the two conversion rates although it exists. The worst case scenario is the third one. The second case is ok since we are not interested in the difference which is less than the threshold we established for the experiment (like 0.01%). The first case is very rare since the two conversion rates are usually different.

There is a difference between the two conversion rates but you don’t have enough sample size (power) to detect it.

The difference between the two conversion rates is too small to be relevant.

There is no difference between the two conversion rates of the control and the variation (they are EXACTLY the same!).

Not rejecting the null hypothesis means one of three things: Rejecting the null hypothesis means your data shows a statistically significant difference between the two conversion rates. Using the statistical analysis of the results, you might reject or not reject the null hypothesis.

The test power : the probability of detecting that difference between the original rate and the variant conversion rates.

Minimum detectable effect : The desired relevant difference between the rates you would like to discover.

It also means that you have significant result difference between the control and the variation with a 95% “confidence.” This threshold is, of course, an arbitrary one and one chooses it when making the design of an experiment.

The significance level for the experiment: A 5% significance level means that if you declare a winner in your AB test (reject the null hypothesis), then you have a 95% chance that you are correct in doing so.

The null hypothesis is tested against the alternative hypothesis which is that the two conversion rates are not equal:īefore we start running the experiment, we establish three main criteria: In every AB test, we formulate the null hypothesis which is that the two conversion rates for the control design ( ) and the new tested design ( ) are equal: Calculating the minimum number of visitors required for an AB test prior to starting prevents us from running the test for a smaller sample size, thus having an “underpowered” test. Any experiment that involves later statistical inference requires a sample size calculation done BEFORE such an experiment starts.