How to Determine A/B Testing Sample Size and Time Frame424
Posted: Sun Jan 19, 2025 5:42 am
Many people often ask how to determine the sample size and timing of A/B testing.
This article will help you confidently kenya b2b leads determine the right sample size and time frame for your next A/B test.
Sample size and time frame for A/B testing
In theory, to run a perfect A/B test and determine a winner between variants A and B, you would have to wait until you had enough results to see if there was a statistically significant difference between them.
Numerous A/B testing experiments prove this to be true.
Depending on the company, the sample size, and how you conduct the A/B test, it can take hours, days, or weeks to get statistically significant results—and you'll have to be patient until you get those results.
Some aspects of marketing require shorter A/B testing times. Take email as an example. With email, waiting for A/B testing to complete can be a problem for several practical reasons, which are listed below.
1. Every email has a limited audience.
Unlike a landing page, once you run an email A/B test, that's it - you can't "add" more people to that A/B test.
So you need to figure out how to get the most out of your emails.
Typically this involves sending an A/B test to a small subset of the list to get statistically significant results, picking a winner, and sending the winning variant to the rest of the list.
2. Running an email marketing program means you send at least a few emails a week.
If you spend too much time collecting results, you may miss sending the next email, which can lead to worse consequences.
3. Sending emails should be timely.
Marketing emails are optimized to be delivered at a specific time of day. Perhaps they support the timing of a new campaign launch and/or arrive in recipients' inboxes at the time they would like to receive them.
So, to run email A/B tests and optimize the messages you send for the best results, consider both the sample size for your A/B testing and the timing of your testing.
How to Determine Sample Size for A/B Testing
1. Check if your contact list is large enough to run an A/B test.
To conduct A/B testing of a sample from a list, you need a list of at least 1000 contacts.
2. Use the sample size calculator.
The A/B Testing Suite has a great free A/B Testing Sample Size Calculator.
3. Enter your base conversion rate, minimum detectable effect, and statistical significance into the calculator.
Statistical significance: This shows how confident you can be that the sample results are within the confidence interval you set. The lower the percentage, the less confident you can be in the results. The higher the percentage, the more people you will need in the sample.
Base Conversion Rate (BCR): BCR is the conversion rate of the control version. For example, if you send an email to 10,000 contacts and 6,000 of them open the email, the base conversion rate (BCR) for opening the email is 60%.
Minimum Detectable Effect (MDE): MDE is the smallest relative change in conversion rate that is required to detect between Version A (the original or control sample) and Version B (the new variant). MDE has a real impact on sample size in terms of time required to run the test and traffic.
4. Depending on the email program you use, you may need to calculate the percentage of the sample size relative to the entire email.
When running an email A/B test, you'll need to choose the percentage of contacts the list will be sent to, not just the initial sample size.
To do this, you need to divide the number of contacts in the sample by the total number of contacts in the list.
Choose the Right Time Frame for Email A/B Testing
When it comes to emails, you need to figure out how long you'll A/B test an email before sending the (winning) version to the rest of your list.
Knowing the time aspect is less statistically dependent, but you should definitely use past data to make more informed decisions. Here's how to do it. If there is no time limit on sending the winning email to the rest of your list, go to analytics.
Find out when your email opens/clicks (or any other success metrics) start to decline. To find out, look at previous emails.
Conclusion
Once you've done these calculations and studied the data, you'll be in a much better position to successfully run A/B tests that are statistically valid and help you achieve your goals.
This article will help you confidently kenya b2b leads determine the right sample size and time frame for your next A/B test.
Sample size and time frame for A/B testing
In theory, to run a perfect A/B test and determine a winner between variants A and B, you would have to wait until you had enough results to see if there was a statistically significant difference between them.
Numerous A/B testing experiments prove this to be true.
Depending on the company, the sample size, and how you conduct the A/B test, it can take hours, days, or weeks to get statistically significant results—and you'll have to be patient until you get those results.
Some aspects of marketing require shorter A/B testing times. Take email as an example. With email, waiting for A/B testing to complete can be a problem for several practical reasons, which are listed below.
1. Every email has a limited audience.
Unlike a landing page, once you run an email A/B test, that's it - you can't "add" more people to that A/B test.
So you need to figure out how to get the most out of your emails.
Typically this involves sending an A/B test to a small subset of the list to get statistically significant results, picking a winner, and sending the winning variant to the rest of the list.
2. Running an email marketing program means you send at least a few emails a week.
If you spend too much time collecting results, you may miss sending the next email, which can lead to worse consequences.
3. Sending emails should be timely.
Marketing emails are optimized to be delivered at a specific time of day. Perhaps they support the timing of a new campaign launch and/or arrive in recipients' inboxes at the time they would like to receive them.
So, to run email A/B tests and optimize the messages you send for the best results, consider both the sample size for your A/B testing and the timing of your testing.
How to Determine Sample Size for A/B Testing
1. Check if your contact list is large enough to run an A/B test.
To conduct A/B testing of a sample from a list, you need a list of at least 1000 contacts.
2. Use the sample size calculator.
The A/B Testing Suite has a great free A/B Testing Sample Size Calculator.
3. Enter your base conversion rate, minimum detectable effect, and statistical significance into the calculator.
Statistical significance: This shows how confident you can be that the sample results are within the confidence interval you set. The lower the percentage, the less confident you can be in the results. The higher the percentage, the more people you will need in the sample.
Base Conversion Rate (BCR): BCR is the conversion rate of the control version. For example, if you send an email to 10,000 contacts and 6,000 of them open the email, the base conversion rate (BCR) for opening the email is 60%.
Minimum Detectable Effect (MDE): MDE is the smallest relative change in conversion rate that is required to detect between Version A (the original or control sample) and Version B (the new variant). MDE has a real impact on sample size in terms of time required to run the test and traffic.
4. Depending on the email program you use, you may need to calculate the percentage of the sample size relative to the entire email.
When running an email A/B test, you'll need to choose the percentage of contacts the list will be sent to, not just the initial sample size.
To do this, you need to divide the number of contacts in the sample by the total number of contacts in the list.
Choose the Right Time Frame for Email A/B Testing
When it comes to emails, you need to figure out how long you'll A/B test an email before sending the (winning) version to the rest of your list.
Knowing the time aspect is less statistically dependent, but you should definitely use past data to make more informed decisions. Here's how to do it. If there is no time limit on sending the winning email to the rest of your list, go to analytics.
Find out when your email opens/clicks (or any other success metrics) start to decline. To find out, look at previous emails.
Conclusion
Once you've done these calculations and studied the data, you'll be in a much better position to successfully run A/B tests that are statistically valid and help you achieve your goals.