Skip to main content

Experimentation Best Practices

Running experiments

Running Your First Experiment

When you’ve finished integrating your experimentation platform (which for GrowthBook, is adding the SDK to your code), it’s time to start running an experiment. We suggest that you first an A/A test to validate your experimentation implementation is correctly splitting traffic, and producing statistically valid results.

Sample Sizes

Understanding experiment power and MDE are important to predict how many samples are required. There are numerous online calculators that can be used to help you predict the sample size. Typical rule of thumb for the lowest number of samples required is that you want at least 100 conversion events per variation. So for example if you have a registration page which has a 10% conversion rate, and you have a 2 way (A and B) experiment that is looking to improve the member registrations, you will want to expose the experiment to at least 2,000 people (1000 per variation).

Test Duration

Due to the natural variability in traffic day to day and hour to hour, experimentation teams will often set a minimum test duration within which a test cannot be called. This helps you avoid optimizing a product for just the users that happen to visit when the test is started. For example, if the weekend traffic of your product is different from the traffic during the week, if you started a test on Friday and ended it on Monday, you may not get a complete picture of the impact your changes have to your weekday traffic. Typical test durations are 1 to 2 weeks, and usually care needs to be taken over holidays.

You may also find that a test would need to run for a month or more to get the power required for the experiment. Very long running tests can be hard to justify as you have to keep the variations of the experiment unchanged for duration, and this may limit your team's velocity towards potentially higher impact changes.

Interaction Effects and Mutual Exclusion

When you start having the ability to run a lot of A/B tests, it can be tempting to not want to run tests in parallel in case they have interaction effects (see above). For example you may want to test a change in the CTA button on your purchase page, and also test changing the price. It can be difficult to figure out if any two tests will meaningfully interact, and many will run the tests in serial in an abundance of caution.

However, meaningful interactions are actually quite rare, and keeping a higher rate of experimentation is usually more beneficial. You can run analysis after the experiments to see if there were any interaction effects which would change your conclusions (GrowthBook is working on an integrated solution for this). If you need to run mutually exclusive tests, you can use GrowthBook’s namespace feature.

Experimentation Frequency

Having a high frequency of A/B testing is important for running a success experimentation program. The main reasons why experimentation frequency is important are:

Maximizing chances: Since success rates are typically low for any given experiment, and large changes are even more rare, by having a high frequency of A/B testing you are maximizing your chance of having impactful experiments.

Continuous improvement: A high frequency of A/B testing allows you to continuously improve your website or application. By testing small changes frequently, you can quickly identify and implement changes that improve user experience, engagement, and conversion rates.

Adaptability: A high frequency of A/B testing allows you to quickly adapt to changes in user behavior, market trends, or other external factors that may impact your website or application. By testing frequently, you can identify and respond to these changes more quickly, ensuring that your site or app remains relevant and effective.

Avoiding stagnation: A high frequency of A/B testing can help you avoid stagnation and complacency. By continually testing and experimenting, you can avoid falling into a rut or becoming overly attached to a specific design or strategy, and instead remain open to new ideas and approaches.


“If you want to have good ideas you must have many ideas. Most of them will be wrong, and what you have to learn is which ones to throw away.”

- Linus Pauling