Question

Take a moment to reflect on what you have learned in this module about hypothesis testing in general and A/B testing in particular. In what other scenarios or industries do you think this type of analysis would be helpful? What precautions should you take when designing such tests? How can you ensure that your results are representative of your target population?

Answer 1

A/B testing is nothing but, two versions (A and B) of a single variable are compared, which are identical except for one variation that might affect a user's behavior. A/B tests are the simplest form of controlled experiment.

On an e-commerce website the purchase funnel is typically a good candidate for A/B testing, as even marginal decreases in drop-off rates can represent a significant gain in sales

It should contain a representative sample and assign the variants A&B randomly

An A/B test hypothesis must always start with a clearly identified problem. A more nuanced approach would involve applying statistical testing to determine if the differences in response rates between A and B were statistically significant (that is, highly likely that the differences are real, repeatable, and not due to random chance.