Statistical models used by A/B testing solutions vary greatly. To interpret your test results with accuracy, you need to be well-versed in the approach your testing solution uses to calculate significance. In this presentation Optimizely stats experts will provide a hard-nosed look at a range of statistical models, the risks and tradeoffs associated with each and explain how not all models are created equal.
Check out these slides to learn:
- How testing solutions use Frequentist and Bayesian models to compute significance
- A refresh on core statistical concepts including significance, error, and more
- How Optimizely’s Stats Engine mitigates risk while allowing experimenters to make decisions quickly