A/B Testing With R

Kevin Feasel

2017-11-27

R

Mira Celine Klein shows how to compare two versions of a feature (or advertising campaign or whatever) to determine if one is better than the other:

In comparison to other methods, conducting an A/B test does not require extensive statistical knowledge. Nevertheless, some caveats have to be taken into account.

When making a statistical decision, there are two possible errors (see also table 1): A Type I error means that we observe a significant result although there is no real difference between our groups. A Type II error means that we do not observe a significant result although there is in fact a difference. The Type I error can be controlled and set to a fixed number in advance, e.g., at 5%, often denoted as α or the significance level. The Type II error in contrast cannot be controlled directly. It decreases with the sample size and the magnitude of the actual effect. When, for example, one of the designs performs way better than the other one, it’s more likely that the difference is actually detected by the test in comparison to a situation where there is only a small difference with respect to the target metric. Therefore, the required sample size can be computed in advance, given α and the minimum effect size you want to be able to detect (statistical power analysis). Knowing the average traffic on the website you can get a rough idea of the time you have to wait for the test to complete. Setting the rule for the end of the test in advance is often called “fixed-horizon testing”.

Click through for more, including a sample with code.  H/T R-Bloggers

Related Posts

From Excel to R: Three Examples

Abdul Majed Raja has a few examples of things which are easy to do in Excel and how you can do them in R: Create a difference variable between the current value and the next valueThis is also known as lead and lag – especially in a time series dataset this varaible becomes very important in feature engineering. In […]

Read More

Calculating AUC in R

Andrew Treadway shows how you can calculate Area Under the Curve in R: AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For […]

Read More

Categories

November 2017
MTWTFSS
« Oct Dec »
 12345
6789101112
13141516171819
20212223242526
27282930