## Size Matters

It would be too easy for someone like me to declare that A/B testing is simple. If you’re doing website testing you have all the power in the world. Sample sizes literally not dreamt of when I was an undergraduate. A large sample is the same thing as power, right? And of course, power is all that matters.

This is completely wrong. While statistical A/B testing is using (parts of) the same toolkit as I was using in RCTs for Papua New Guinea and Tonga: it isn’t looking at the same problem and it isn’t looking at the same effect sizes. A/B testing is used for incremental changes in this context. On the contrary, in my previous professional life we were looking for the biggest possible bang we could generate for the very limited development dollar.

As always, context matters. Power gets more expensive as you approach the asymptote if effect size is also shrinking. How expensive? How big does that sample have to be? This is one of the things I’m looking at in this experiment.

However, size isn’t something I see discussed often. The size of the test is about control: it tells you what your tradeoff has been between Type I and Type II errors. We fix our size a priori and then we move on and forget about it. But is our fixed, set size the same as the actual size of our test?

We know that Fisher’s exact test is often actually undersized in practice, for example.  In practice, this means the test is too conservative. On the flip side, a test that is too profligate (over sized) is rejecting the null hypothesis when it’s true far too often.

In this post, I’m going to look at a very basic test with some very robust assumptions and see what happens to size and power as we vary those assumptions and sample sizes. The purpose here is the old decision vs default adage: know what’s happening and own the decision you make.

# tl;dr

While power matters in A/B testing, so does the size of the test. We spend a lot of time worrying about power in this field but not enough (in my view) ensuring our expectations of size are appropriate. Simple departures from the unicorns-and-fairy-dust normal distribution can cause problems for size and power.

# Testing tests

The test I’m looking at is the plainest of plain vanilla statistical tests. The null hypothesis is that the mean of a generating distribution is zero against an alternate. The statistic is the classic z-statistic and the assumptions underlying the test can go one of two ways:

1. In the finite sample, if the underlying distribution is normal, then the z-statistic is normal, no matter the sample size.
2. Asymptotically, as long as the underlying distribution meets some conditions like finite fourth moments (more on this later- fat tails condition) and independence, as the sample size gets large, the z-statistic will have a normal limiting distribution.

I’m going to test this simple test in one of four scenarios over a variety of sample sizes:

1. Normally generated errors. Everything is super, nothing to see here. This is the statistician’s promised land flowing with free beer and coffee.
2. t(4) errors. This is a fat tailed distribution still centred on zero and symmetric. The fourth moment is finite, but no larger moments are. Is fat-tails alone an issue?
3. Centred and standardised chi squared (2) errors: fat tailed and asymmetric, the errors generated from this distribution have mean zero and a standard deviation of unity. Does symmetry matter that much?
4. Cauchy errors. This is the armageddon scenario: the central limit theorem doesn’t even apply here. There is no theoretical underpinning for getting this test to work under this scenario- there are no finite moments at or equal to the first or above (there are some fractional ones though). Can a big sample size get you over this in practice?

In the experiments, we’ll look at sample sizes between 10 and 100 000. Note that the charts below are on a log10 scale.

The null hypothesis is mu =0, the rejection rate in this scenario gives us the size of our test. We can measure power under a variety of alternatives and here I’ve looked at mu = 0.01, 0.1, 1 and 10. I also looked at micro effect sizes like 0.0001, but it was a sad story. [1]

The test has been set with significance level 0.05 and each experiment was performed with 5000 replications. Want to try it yourself/pull it apart? Code is here.

# Normal errors: everything is super

With really small samples, the test is oversized. N=10 is a real Hail Mary at the best of times, it doesn’t matter what your assumptions are. But by about n=30 and above it’s consistently in the range of 0.05.

It comes as a surprise to no one to hear that power is very much dependent on distance from the null hypothesis. For alternates where mu =10 or mu =1, power is near unity for smallish sample sizes. In other words, we reject the null hypothesis close to 100% of the time when it is false. It’s easy for the test to distinguish these alternatives because they’re so different to the null.

But what about smaller effect sizes? If mu =0.1, then you need at least a sample of a thousand to get close to power of unity. If mu =0.001 then a sample of 100 000 might be required.

Smaller still? The chart below shows another run of the experiment with effect sizes of mu =0.001 and 0.0001 – miniscule. The test has almost no power even at sample sizes of 100 000. If you need to detect effects this small, you need samples in the millions (at least).

# Fat shaming distributions

Leptokurtotic distributions (fat tails) catch a lot of schtick for messing up tests and models. And that’s fairly well deserved. However, degree of fatness is an issue. The t(4) distribution still generates a statistic that works asymptotically in this context: but it has only exactly the number of moments we need and no wriggle room at all.

Size is more variable than in the normal scenario: still comfortably around 0.05, it’s more profligate at small sample sizes and at larger ones has a tendency to be more conservative. It’s still reasonably close to 0.05 at larger sample sizes, however.

Power is costly for the smaller effect sizes. For the same sample size (say n=1000) with mu = 0.1, there is substantially less power than in the normal case. A similar behaviour is evident for mu=0.01. Tiny effect sizes are similarly punished (see below).

# Fat, Skewed and Nearly Dead

The chi-squared(2) distribution put this simple test through its paces. The size of the test for anything under n=1000 is not in the same ballpark as its nominal, rendering the test (in my view) a liability. By the time n=100 000, the size of the test is reasonable.

Power, in my view, while showing similar outcomes is not a saving grace here: there’s very little control in this test under which you can interpret your power.

# Here be dragons

I included a scenario with the Cauchy distribution, despite the fact it’s grossly unfair to this simple little test. The Cauchy distribution ensures that the Central Limit Theorem does not apply here: the test will not work in theory (or, indeed, in practice).

I thought it was a useful exercise, however, to show what that looks like. Too often, we assume “as n gets big CLT is going to work its magic” and that’s just not true. To whit: one hot mess.

Neither size nor power is improved with sample size increasing: that’s because the CLT isn’t operational in this scenario. The test is under sized, under powered for all but the largest of effect sizes (and really, at that effect size you could tell a difference from a chart anyway).

# A/B reality is rarely this simple

A/B testing reality is rarely as simple as the test I’ve illustrated above. More typically, we’re testing groups of means or  proportions and interaction effects, possibly dynamic relationships and a long etc.

My purpose here is to show that even a simple, robust test with minimal assumptions can be thoroughly useless if those assumptions are not met. More complex tests and testing regimes that build on these simple results may be impacted more severely and more completely.

Power is not the only concern: size matters.

# End notes

[1] Yes, I need to get a grip on latex in WordPress sooner or later, but it’s less interesting than the actual experimenting.

## Violence Against Women: Standing Up with Data

Today, I spent the day at a workshop tasked with exploring the ways we can use data to contribute to ending violence against women. I was invited along by The Minerva Collective, who have been working on the project for some time.

Like all good workshops there were approximately 1001 good ideas. Facilitation was great: the future plan got narrowed down to a manageable handful.

One thing I particularly liked was that while the usual NGO and charitable contributors were present (and essential) the team from Minerva had managed to bring in a number of industry contributors from telecommunications and finance who were able to make substantial contributions. This is quite a different approach to what I’ve seen before and I’m interested to see how we can work together.

I’m looking forward to the next stage, there’s a huge capacity to make a difference. While there are no simple answers or magic bullets, data science could definitely do some good here.