Practical Considerations for Email:

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Factors influencing sample size:

Baseline Conversion Rate (Open Rate): Your historical average open rate. Lower open rates require larger samples.
Minimum Detectable Effect (MDE): The smallest difference in open rate you want to be able to reliably detect. A smaller MDE requires a larger sample size.
Statistical Significance Level (Alpha): Typically set at 95% (p-value < 0.05). This means there's a 5% chance the observed difference is due to random chance.
Statistical Power (Beta): Typically set at 80% or usa email list 90%. This is the probability of detecting a real effect if one exists.
Using A/B Test Calculators: Don't try to calculate this manually. Use online A/B test sample size calculators (e.g., Optimizely, VWO, or simple online statistical calculators). You'll input your baseline open rate, desired MDE, and significance level.


For large lists (tens of thousands or more), a test group of 5-10% of your total audience per variation is common, but ensure it meets statistical significance requirements.
For smaller lists (a few thousand), you might need a larger percentage or even the full list for the test to get meaningful results over time.
Many ESPs have built-in A/B testing features that recommend sample sizes or run the test until significance is reached.
Step 4: Run the Test
With your hypothesis, variables, and sample size determined, it's time to execute.

Setup in your ESP: Most modern ESPs (Mailchimp, ConvertKit, ActiveCampaign, HubSpot, Salesforce Marketing Cloud, etc.) have integrated A/B testing functionalities. You'll upload your email content, create your subject line variations, define the test segment size, and set the duration or the condition for declaring a winner.