I am running A/B tests for our new digital marketing landing pages and I'm confused about the trade-offs between Type I and Type II errors. If I lower my significance level to avoid false positives, am I significantly increasing the risk of missing a winning variation? How do you guys balance these risks in a high-stakes corporate environment?
3 answers
In a business context, a Type I error (false positive) means you implement a change thinking it works when it actually doesn't, wasting resources. A Type II error (false negative) means you discard a winning strategy because the test didn't show significance. Balancing them requires looking at your "Power." Usually, we aim for a power of 0.80, meaning a 20% chance of a Type II error. If the cost of a wrong implementation is massive, be strict with your alpha (Type I). If you are in a startup and need to find any edge possible, you might tolerate a higher alpha to avoid missing out.
Are you calculating your required sample size before starting the test? Without a pre-determined sample size based on the Minimum Detectable Effect, you'll likely struggle with both error types.
Think of it as a see-saw; as you push down the risk of a false positive, the risk of a false negative naturally goes up. You need to decide which mistake is more expensive for your brand.
Exactly, Christopher. It's all about risk management. Most firms prioritize avoiding Type I errors to protect their current baseline conversion rates from negative impacts.
Jennifer, I haven't been doing that consistently. I usually just let the test run for two weeks and check the dashboard. Does the sample size calculation actually help in reducing the probability of a Type II error specifically, or does it just help with the overall confidence level?