A/B testing

See also: Machine learning terms

Introduction

A/B testing is a statistical method employed in machine learning research to compare two versions of a product and determine which version is more successful. It involves randomly dividing a population into two groups, "A" and "B," then exposing each group to one version of the tested product. After analyzing the results of this experiment, one version will be determined as having higher click-through rates or conversion rates, respectively.

Methodology

A key to conducting an effective A/B test is ensuring the two groups, A and B, are as similar in demographic and behavioral characteristics as possible. This can usually be accomplished through random assignment of individuals to each group. Once formed, each group is then exposed to one version of the product being tested.

After testing, statistical methods like hypothesis testing or regression analysis are employed to determine which version of the product is more successful in reaching its desired outcomes. The purpose is to establish whether observed differences are due solely to chance or statistically significant and thus likely caused by differences between products being tested.

Considerations

When designing an A/B test, it is essential to take into account factors like sample size for testing, test duration and analysis technique used.

When conducting a statistical significance test, the sample size should be large enough to guarantee accurate results but not so large that it becomes impractical or time-consuming. Furthermore, the length of the experiment must be carefully considered since it will impact both the amount of data collected and its accuracy.

Finally, the type of analysis used to analyze results will influence their validity. Regression analysis can be employed to account for other variables that could have an impact on outcomes such as time of day or week of testing, while hypothesis testing can be utilized to verify if observed differences are statistically significant.

Limitations

Although A/B testing can be a powerful tool, there are some limitations to consider. A large enough sample size is necessary for statistical significance and the outcomes may be affected by factors beyond the experimenter's control, such as external events or seasonal fluctuations. Furthermore, A/B testing may not be suitable for testing complex changes like those to user workflows or product features.

Explain Like I'm 5 (ELI5)

Say you have two toys to play with: a toy car and a dinosaur. Both are great fun, but you can't decide which one you like better. To help make up your mind, try playing with one for several days then switching it up for another few days. After one week has passed, see which toy provided more enjoyment and ultimately decide which one you prefer.

A/B testing in machine learning is similar to playing with toys; instead of testing two distinct computer programs or websites, we show one version to some people and the other version to others, to see which works better. After some time has passed, we can determine which version people preferred more and decide which one should be used moving forward.