Frequentist approaches to AB/Hypothesis testing are notoriously hard to understand - even for some statisticians. Bayesian methods tend to be more difficult computationally and mathematically, but in terms of interpretability they easily take the cake. Long gone are the days of ‘rejecting the null hypothesis with a p-value of .043’. Rejoice in phrases such as ‘The probability that A has a 3% lift over B is 96.4%’.
bayesAB is an R package which provides a suite of functions for a user to conduct and interpret the results of a count or proportion AB test in a Bayesian way. The package is meant to be used at all steps of the process - from choosing a prior, to interpreting final results, and then calculating lifts based on certain thresholds.
Non technical users may simply use these methods as drop-in replacements for the t.test and prop.test in R. Data-minded people may opt to read some of the help documentation and play with some of the helper functions.
- Scrape ESPN for all player photos
- Extract and normalize their faces
- Build a Convolutional Neural Network to match their faces with the sport they play
- Achieve 93% accuracy on holdout set