#1 - “Optimizely encourages you to stop the test as soon as it reaches ‘statistical significance.’” - This actually isn’t true. We recommend you calculate your sample size before you start your test using a statistical significance calculator and waiting until you reach that sample size before stopping your test. We wrote a detailed article about how long to run a test, here: https://help.optimizely.com/hc/en-us/articles/200133789-How-...
We also have a sample size calculator you can use, here: https://www.optimizely.com/resources/sample-size-calculator
#2 - Optimizely uses a one-tailed test, rather than a 2-tailed test. - This is a point the article makes and it came up in our customer community a few weeks ago. One of our statisticians wrote a detailed reply, and here’s the TL;DR:
- Optimizely actually uses two 1-tailed tests, not one.
- There is no mathematical difference between a 2-tailed test at 95% confidence and two 1-tailed tests at 97.5% confidence.
- There is a difference in the way you describe error, and we believe we define error in a way that is most natural within the context of A/B testing.
- You can achieve the same result as a 2-tailed test at 95% confidence in Optimizely by requiring the Chance to Beat Baseline to exceed 97.5%.
- We’re working on some exciting enhancements to our methodologies to make results even easier to interpret and more meaningfully actionable for those with no formal Statistics background. Stay tuned!
Here’s the full response if you’re interested in reading more: http://community.optimizely.com/t5/Strategy-Culture/Let-s-ta...
Overall I think it’s great that we’re having this conversation in a public forum because it draws attention to the fact that statistics matter in interpreting test results accurately. All too often, I see people running A/B tests without thinking about how to ensure their results are statistically valid.
Dan