**Frank Portman**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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*This is a teaser blog post to showcase my new R package, bayesAB. Below is the intro of the knitted HTML of the introduction.Rmd vignette of the package. Formatting/colors may be a bit off since the vignette has it’s own styling. Check the full vignette here. Check it out on Github here. Newer version has since been released.*

Most A/B test approaches are centered around frequentist hypothesis tests used to come up with a point estimate (probability of rejecting the null) of a hard-to-interpret value. Oftentimes, the statistician or data scientist laying down the groundwork for the A/B test will have to do a `power test`

to determine sample size and then interface with a Product Manager or Marketing Exec in order to relay the results. This quickly gets messy in terms of interpretability. More importantly it is simply not as robust as A/B testing given informative priors and the ability to inspect an entire distribution over a parameter, not just a point estimate.

**Enter Bayesian A/B testing.**

Bayesian methods provide several benefits over frequentist methods in the context of A/B tests – namely in interpretability. Instead of p-values you get direct probabilities on whether A is better than B (and by how much). Instead of point estimates your posterior distributions are parametrized random variables which can be summarized any number of ways. Bayesian tests are also immune to ‘peeking’ and are thus valid whenever a test is stopped.

This document is meant to provide a brief overview of the **bayesAB** package with a few usage examples. A basic understanding of statistics (including Bayesian) and A/B testing is helpful for following along.

## Methods

Unlike a frequentist method, in a Bayesian approach you first encapsulate your **prior** beliefs mathematically. This involves choosing a distribution over which you believe your parameter might lie. As you expose groups to different tests, you collect the **data** and combine it with the **prior** to get the **posterior** distribution over the parameter(s) in question. Mathematically, you are looking for `P(parameter | data)`

which is a combination of the **prior** and **posterior** (the math, while relatively straightforward, is outside of the scope of this brief intro).

As mentioned above, there are several reasons to prefer Bayesian methods for A/B testing (and other forms of statistical analysis!). First of all, interpretability is everything. Would you rather say “P(A > B) is 10%”, or “Assuming the null hypothesis that A and B are equal is true, the probability that we would see a result this extreme in A vs B is equal to 3%”? I think I know my answer. Furthermore, since we get a probability distribution over the parameters of the distributions of A and B, we can say something such as “There is a 74.2% chance that A’s \(\lambda\) is between 3.7 and 5.9.” directly from the methods themselves.

Secondly, by using an informative prior we alleviate many common issues in regular A/B testing. For example, *repeated testing* is an issue in A/B tests. This is when you repeatedly calculate the hypothesis test results as the data comes in. In a perfect world, if you were trying to run a Frequentist hypothesis test in the most correct manner, you would use a `power test calculation`

to determine sample size and then not peek at your data until you hit the amount of data required. Each time you run a hypothesis test calculation, you incur a probability of *false positive*. Doing this repeatedly makes the possibility of any single one of those ‘peeks’ being a *false positive* extremely likely. An informative prior, means that your posterior distribution **should** make sense any time you wish to look at it. If you ever look at the **posterior** distribution and think “this doesn’t look right!”, then you probably weren’t being fair with yourself and the problem when choosing *priors*.

Furthermore, an informative prior will help with the *low base-rate problem* (when the probability of a success or observation is very low). By indicating this in your priors, your posterior distribution will be far more stable right from the onset.

One criticism of Bayesian methods is that they are computationally slow or inefficient. **bayesAB** leverages the notion of *conjugate priors* to sample from analytical distributions very quickly (1e6 samples in <1s).

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**Frank Portman**.

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