When running A/B tests to improve your conversion rate, it is highly recommended to calculate a** sample size** before testing and measure your **confidence interval**.

This advice comes from old-fashioned industries (agriculture, pharmaceutical…) where it’s important to know your confidence level. This factor significantly influences the experiment costs that we want to keep as low as possible.

**This is what sample size calculators are used for**. You are asked for the current success rate (conversion rate) and the size of the minimum effect to be measured. The result of the calculation is the population size needed to conclude from such an experiment.

This transposes badly in the “digital area” for three main reasons:

- Measuring conversions costs nothing (unlike in the industry).
- The number of visitors is a part of the problem (not the answer).
- The effect of variation is difficult to predict (in practice, this is precisely the question you’re asking yourself!).

This makes it very difficult to use sample size calculators. So, our data scientists AB Tasty have developed a **Minimum Detectable Effect Calculator (MDE)**.

Just enter the number of visitors you have on your site and the conversion rate of the page you want to test!

## Minimum Detectable Effect Calculator

## A/B Test Calculator

Calculate the minimum sample size as well as the ideal duration of your A/B tests based on your audience, conversions, and other factors like the Minimum Detectable Effect.

### How many users do you need?

### How long should your A/B test run?

Our **A/B test calculator** also gives you an idea of the duration of your A/B test. For this** test duration calculator** to work, please fill in the information above, as well as your average daily traffic on the tested page, and your number of variations – including the control version. Read more about confidence intervals and methods to interpret test results.

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these customers ?

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