A/B testing, also known as split testing, is a statistical method used to compare two or more variants to determine which version performs better for a given objective.
Imagine you are selling lemonade and you can’t decide if your sign should say “Delicious Lemonade” (version A) or “Refreshing Lemonade” (version B). So, you try both! For the first 100 customers, you show the “Delicious” sign and for the next 100 customers, you show the “Refreshing” sign. Then you check which sign attracted more customers. This is pretty much what A/B testing is.
In a machine learning context, A/B testing is a method used to compare two versions of a model, algorithm, or data processing technique to find out which one has improved performance. Usually, two variants, A (control) and B (challenger), are tested on different segments of users over the same period of time. The variant that achieves better results based on a predefined metric (or set of metrics), such as click rates in online marketing, accuracy of predictions in machine learning algorithms, or overall user engagement, is considered superior.
In order to ascertain that the results that are being seen are due to the differences in the variants, and not due to extraneous variables, careful experimental design is necessary. Randomization is used to ensure that each user has an equal chance of being exposed to each variant, thus reducing the potential for confounding factors to bias the results.
Furthermore, to ascertain that the difference in performance is indeed statistically significant and not due to chance, hypothesis testing is conducted. If there’s a higher confidence (say 95% or 99%) that B is better than A, then it’s inferred that B is indeed better.
One potential complication with A/B testing in the context of AI/ML is the cold start problem. This problem arises because the learning algorithms don’t have prior information to start predicting from and cannot provide accurate results until they have had sufficient input. To solve it, researchers often use techniques like epsilon-greedy method where epsilon fraction of the time a random variant is selected, and the best performing one for the rest of the time.
Correct interpretation of A/B testing outcomes assumes that everything else remains constant apart from the variants being tested. Thus, the conclusion drawn solely depends on the variant and not on external factors making it more reliable.
Hypothesis Testing, Experimental Design, Control Group, Challenger Group, Randomization, Null Hypothesis, Cold Start Problem, Epsilon-Greedy Method