Mark Needham

Thoughts on Software Development

R: Modelling a conversion rate with a binomial distribution

As part of some work Sid and I were doing last week we wanted to simulate the conversion rate for an A/B testing we were planning.

We started with the following function which returns the simulated conversion rate for a given conversion rate of 12%:

```generateConversionRates <- function(sampleSize) { sample_a <- rbinom(seq(0, sampleSize), 1, 0.12) conversion_a <- length(sample_a[sample_a == 1]) / sampleSize   sample_b <- rbinom(seq(0, sampleSize), 1, 0.12) conversion_b <- length(sample_b[sample_b == 1]) / sampleSize   c(conversion_a, conversion_b) }```

If we call it:

```> generateConversionRates(10000) [1] 0.1230 0.1207```

We have a 12.3% conversion rate on A and a 12.07% conversion rate on B based on 10,000 sample values.

We then wrote the following function to come up with 1000 versions of those conversion rates:

```generateSample <- function(sampleSize) { lapply(seq(1, 1000), function(x) generateConversionRates(sampleSize)) }```

We can call that like this:

```> getSample(10000) [[998]] [1] 0.1179 0.1216   [[999]] [1] 0.1246 0.1211   [[1000]] [1] 0.1248 0.1234```

We were then using these conversion rates to try and work out how many samples we needed to include in an A/B test to have reasonable confidence that it represented the population.

We actually ended up abandoning that exercise but I thought I’d record the code because I thought it was pretty interesting.

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Written by Mark Needham

February 7th, 2013 at 1:26 am

Posted in R

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