# Learning Data Science: A/B Testing in Under One Minute

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Google does it! Facebook does it! Amazon does it for sure!

Especially in the areas of web design and online advertising, everybody is talking about *A/B testing*. If you quickly want to understand what it is and how you can do it with R, read on!

The basic idea of A/B testing is to systematically (and normally automatically) test *two different alternatives*, e.g. two different web designs, and decide which one does better, e.g. in terms of *conversion rate* (i.e. how many people click on a button or buy a product):

The bad news is, that you have to understand a little bit about *statistical hypothesis testing*, the good news is that if you read the following post, you have everything you need (plus, as an added bonus R has all the tools you need already at hand!): From Coin Tosses to p-Hacking: Make Statistics Significant Again! (ok, reading it would make it over one minute…).

To give you a practical example we will use a dataset from DataCamp’s course on “A/B Testing in R” (experiment_data.csv), which shows whether each group (`control`

and `test`

group) either clicked on the respective offer… or not (`clicked_adopt_today`

):

experiment <- read.csv("data/experiment_data.csv") experiment <- experiment[ , 2:3] head(experiment, 10) ## condition clicked_adopt_today ## 1 control 0 ## 2 control 1 ## 3 control 0 ## 4 control 0 ## 5 test 0 ## 6 test 0 ## 7 test 1 ## 8 test 0 ## 9 test 0 ## 10 test 1

Let us create two tables with the *absolute* and the *relative proportions*:

prop <- table(experiment) prop_abs <- addmargins(prop) prop_abs ## clicked_adopt_today ## condition 0 1 Sum ## control 245 49 294 ## test 181 113 294 ## Sum 426 162 588 prop_rel <- prop.table(prop, 1) prop_rel <- round(addmargins(prop_rel, 2), 2) prop_rel ## clicked_adopt_today ## condition 0 1 Sum ## control 0.83 0.17 1.00 ## test 0.62 0.38 1.00

Now for the actual test: conveniently enough, R has the `prop.test`

function, which tests whether two proportions are *significantly* different (by performing a so-called *Pearson’s chi-squared test* under the hood). We only have to put our original table into the function and R does the rest for us:

prop.test(prop) ## ## 2-sample test for equality of proportions with continuity ## correction ## ## data: prop ## X-squared = 33.817, df = 1, p-value = 6.055e-09 ## alternative hypothesis: two.sided ## 95 percent confidence interval: ## 0.1442390 0.2911352 ## sample estimates: ## prop 1 prop 2 ## 0.8333333 0.6156463

Voilà, that was it already! Because the *p-value* is way below the common threshold of 0.05 the difference is highly significant, so we can reject the *null hypothesis* (that the difference is just due to chance)!

As a consequence, we would definitely go for the design that was presented to the test group in the future.

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