Posted in A/B Split Testing on January 26th, 2010
Recently, I published an A/B split testing case study where an eCommerce store reduced bounce rate by 20%. Some of the blog readers were worried about statistical significance of the results. Their main concern was that a value of 125-150 visitors per variation is not enough to produce reliable results. This concern is a typical by-product of having superficial knowledge of statistics which powers A/B (and multivariate) testing. I’m writing this post to provide an essential primer on mathematics of testing so that you never jump to a conclusion on reliability of a test results simply on the basis of number of visitors.
What exactly goes behind A/B split testing?
Imagine your website as a black box containing balls of two colors (red and green) in unequal proportions. Every time a visitor arrives on your website he takes out a ball from that box: if it is green, he makes a purchase. If the ball is of red color, he leaves the website. This way, essentially, that black box decides the conversion rate of your website.
A key point to note here is that you cannot look inside the box to count the number of balls of different colors in order to determine true conversion rate. You can only estimate the conversion rate based on different balls you see coming out of that box. Because conversion rate is an estimate (or a guess), you always have a range for it; never a single value. For example, mathematically, the way you describe a range is:
“Based on the information I have, 95% of the times conversion rate of my website ranges from 4.5%-7%.”
As you would expect, with more number of visitors, you get to observe more number of balls. Hence, your range gets narrower and your estimate starts approaching true conversion rate.
The maths of A/B split testing
Mathematically, the conversion rate is represented by a binomial random variable, which is a fancy way of saying that it can have two possible values: conversion or non-conversion. Let’s call this variable as p. Our job is to estimate the value of p and for that we do n trials (or observe n visits to the website). After observing those n visits, we calculate how many visits resulted in a conversion. That percentage value (which we represent from 0 to 1 instead of 0% to 100%) is the conversion rate of your website.
Now imagine that you repeat this experiment multiple times. It is very likely that, due to chance, every single time you will calculate a different value of p. Having all (different) values of p, you get a range for the conversion rate (which is what we want for next step of analysis). To avoid doing repeated experiments, statistics has a neat trick in its toolbox. There is a concept called standard error, which tells how much deviation from average conversion rate (p) can be expected if this experiment is repeated multiple times. Smaller the deviation, more confident you can be about estimating true conversion rate. For a given conversion rate (p) and number of trials (n), standard error is calculated as:
Standard Error (SE) = Square root of (p * (1-p) / n)
Without going much into details, to get 95% range for conversion rate multiply the standard error value by 2 (or 1.96 to be precise). In other words, you can be sure with 95% confidence that your true conversion rate lies within this range: p % ± 2 * SE
(In Visual Website Optimizer, when we show conversion rate range in reports, we show it for 80%, not 95%. So we multiply standard error by 1.28)
What does it have to do with reliability of results?
In addition to calculating conversion rate of the website, we also calculate a range for its variations in an A/B split test. Because we have already established (with 95% confidence) that true conversion rate lies within that range, all we have to observe now is the overlap between conversion rate range of the website (control) and its variation. If there is no overlap, the variation is definitely better (or worse if variation has lower conversion rate) than the control. It is that simple.
As an example, suppose control conversion rate has a range of 6.5% ± 1.5% and a variation has range of 9% ± 1%. In this case, there is no overlap and you can be sure about the reliability of results.
Do you call all that math simple?
Okay, not really simple but it is definitely intuitive. To save the trouble of doing all the math by yourself, either use a tool like Visual Website Optimizer which automatically does all the number crunching for you. Or, if you are doing a test manually (such as for Adwords), use our free A/B split test significance calculator.
So, what is the take-home lesson here?
Always, always, always use an A/B split testing calculator to determine significance of results before jumping to conclusions. Sometimes you may discount significant results as non-significant solely on the basis of number of visitors (such as you may do for this case study). Sometimes you may think results are significant due to large number of visitors when in fact they are not (such as here). You really want to avoid both scenarios, don’t you?
Stay up to date
- RT @unbounce: A/B Testing Between Free and Paid Signups: Sometimes Free is Better: http://t.co/MtiATLWOeQ by @wingify #
- Interesting results from our new case study - "A/B Testing Between Free and Paid Signups: Sometimes Free is Better" http://t.co/gmd9jyBFwA #
- @JuliaFok Our $129/mo package that gives 30,000 visitors to test seems like a good fit for you. #
- New post on our blog: Extend Your Conversion Funnel Beyond the "Thank-You" Page http://t.co/rEEtlVrxv9 #
- RT @SantiagoEcomm: @Wingify's #SplitTesting blog is great resource to follow for knowledge & case studies on #ConversionRateOptimisation ht… #
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