The question “How to test if my website has a small number of users” comes up frequently in A/B testing. There are different opinions on the topic ranging from altering the significance threshold, statistical power or the minimum effect of interest all the way to giving up on testing altogether. This article discusses if it is worth to perform A/B tests with small numbers of users and talks about ways to go about it in an efficient way.

**Why do we A/B test?**

Let us start with a brief refresher on the two main reasons we might want to do a split test in the first place:

- To
**mitigate / manage the risk**of a poor decision. E.g. we want to manage the risk of releasing an update to our shopping cart funnel which will reduce our average revenue per user. - To
**estimate the effect size and direction**(positive or negative lift) of a proposed change to the website or app. For example, we might want to know how much does including 5 additional photos per product increase our add to cart rate and/or purchase rate.

The duality of these two reasons is obvious since what is a “poor decision” is usually defined in terms of the actual effect size and effect direction of a proposed change. In the case of the shopping cart funnel update we would be happy if the *estimate *suggests that we won’t see a reduction in average revenue per user versus our existing one. In the case of the additional photos, if we assume the additional cost of taking, processing and hosting the extra photos would pay for itself if we see a 0.4% relative lift in add to the purchase rate we can frame it as a *risk mitigation* task: we want to manage the risk of making the poor decision of adding extra photos to all our products while they in fact lead to an increase in lift of less than 0.4%.

Regardless of how the goal is framed, conversion rate optimization experts always have a consideration for the return on investment (ROI) of the particular test when deciding whether to perform it and the what its statistical design should be. Weighing the costs and benefits is usually done through a risk-reward analysis which is where the small sample size needs to be factored in.

**Small sample size: an issue for big business as well**

Before proceeding to the meat of the matter let’s make an important point:

The issue of having a very limited number of users to test with is not limited to small businesses.While you certainly qualify for the small sample size case if your website only gets a couple of thousand of users per month or if your e-mail marketing list contains a couple of thousand emails, large and even gigantic corporations like Google, Microsoft, Amazon, Facebook, Netflix, Booking, etc. also face similar issues in many cases, despite the hundreds of millions of users they have to work with.

Consider, for example, that you are tasked with improving the UX of a feature which is only used by 0.2% of the users of the website or app which might see an overall usage of a hundred million users per month. For example, you might be working on the signup for advertisers. If advertisers are 0.2% of total users, this immediately limits the sample size you can work with to 200,000.

Since the signup is for new users only, we are down to 4,000 users per month who visit the ad signup section and are not customers already. Consider further, that the user experience you want to improve concerns only users on desktop devices – say 60% of total. You are down to a mere 2,400. Suddenly, **you are in small sample size territory for this particular A/B test despite the 100 million overall users to the website/app**.

The above example is with fictitious numbers, but one can easily find many real cases where the segment for which the user experience is to be improved is much smaller than the overall number of users to a website or app. In such cases it shares many of the issues and potential solutions faced by a small business. The most obvious difference between the above scenario and a small business is the availability of a testing platform and testing experts and know-how which are already in place and their cost is thus lower, but even that is not always the case.

**Problems with having a small number of users to test on**

CROs and business owners usually become aware of the main problem of having a small number of users or sessions when they perform a sample size calculation and it reveals that it would take them many months (if not years) to perform a test with the chosen parameters, assuming the traffic stays at about the same level.

It is typical to perform the sample size calculation using the default 95% statistical significance and 80% statistical power and a minimum effect of interest of 5-10%, which would **require between 50,000 and 500,000 users.** The above numbers assume a baseline metric is a conversion rate between 2% and 5% and a fixed sample size test with only one variant tested vs. a control. You can use this statistical calculator to perform sample size calculations for different scenarios.

If, however, you properly understand what minimum effect of interest means and if even a 0.5% lift would be exciting to see from a given test, then the required sample size skyrockets to **millions or tens of millions**.

While bigger e-commerce websites may see 500,000 or 1,000,000 users in a month, a lot of small and medium-sized online merchants, SaaS businesses, consultant business, etc. may not see that many users in a whole year or even over several years. Testing for a month is something most would be on board with, but waiting for a year or several years to complete a test is not. The situation gets even worse when you want to test a change which only affects a segment of your overall users and even large businesses might end up having to deal with a small sample size, as illustrated in the previous section.

So, **the main problem so far seems to be that A/B tests would take a lot of time to complete making testing seem impractical**.

*Is that really so?*

Remember that we only reached this conclusion since we went with the “default” and “what seems reasonable” in terms of setting our error limits and minimum effect of interest. There were no calculations to back these up, to ensure that they are optimal in terms of business results, at least to the extent we can measure them.

Furthermore, we assumed that we are using a fixed-sample size test procedure and that the metric we are measuring performance by is the optimal one.

Let us see how challenging these assumptions in a more rigorous way might help us run ROI-positive tests even with a small sample size.

**How to A/B test with a small sample size?**

Let us see what possible ways there are to address the small sample size problem as explained above.

**1.) Skip A/B testing?**

Remember that we are testing for the primary reasons of estimation and business risk management. If none of these is a concern, there is scarcely any reason to A/B test. I can hardly think of any such situation aside from fixing obvious technical issues where the perceived risk of the fixes causing worse issues than the one they address is practically non-existent. In such scenarios you are likely better off skipping A/B testing entirely and simply implementing the fix.

Skipping the A/B test is something you should consider in such situations even if you have huge numbers of users to work with.

**2.) Accept higher uncertainty**

It should be well-known that we can **lower the required sample size** by:

- Reducing the confidence threshold / increasing the significance threshold – accepting higher risk of false positives;
- Increasing the minimum effect of interest (MEI/MDE) – higher false negative rate;
- Accepting the test will have lower power against a particular MEI – higher false negative rate;

The second and third option are equivalent and interchangeable. All three result in higher uncertainty and greater risk of either false positives or false negatives.

The question is how much uncertainty of either type we can **tolerate **and still be **happy with the trade-off between certainty and speed of testing** (sample size). In other words, will it be better to test for 12 weeks if this way you have 80% probability of seeing a statistically significant outcome at the 95% confidence level if the true effect is larger than X, or should you instead run the test for only 6 weeks with a confidence level of 80%? Or maybe you should test with 80% confidence threshold for 12 weeks to increase statistical power and thus reduce the risk of a false negative?

It is actually quite hard to answer this question due to circular feedback from tweaking the parameters and the fact that the answer requires encoding intuitions and predictions about the effect size into a probability distribution / frequency distribution.

**3.) Use a sequential testing procedure**

Using sequential testing statistics is a long-term strategy which can result in 20%-80% faster tests, meaning that a sequential test would, on average, require 20% to 80% fewer users to conduct compared to an equivalent fixed-sample size test.

There are trade-offs in sequential testing, too. Under the worst possible circumstances certain tests might continue on for longer than an equivalent fixed sample size test would have taken.

**4.) Use conversion to next step as primary KPI**

See if it makes sense to measure the outcome from the step you are optimizing in terms of people who make it to the next step, e.g. adding a product to cart or starting the checkout, instead of in terms of the final measurable outcome, e.g. purchase rate or average revenue per user.

Without going into much detail as you can verify this using the sample size calculator suggested above, the reasoning is that the signal to noise ratio in that metric will be better than the traditional one and thus allow for faster testing.

As with the other 3 options above, there is a trade-off: the metric you are using is not the one closest to the business bottom-line which means that the effect might dissipate, entirely disappear or even reverse itself on following steps. Usually there’s a low probability of this happening, but there are also situations in which it would be a great mistake to make those assumptions, e.g. when you use discounts to increase the add to cart and/or checkout start conversion rate.

**No free lunch**

As you can see, no matter if you go for 1, 2, 3, or 4, or a combination of them, there is always a trade-off. There is simply no such thing as a free lunch.

**Economics of testing: small sample size vs. large sample size**

Many people are under the wrong impression that as long as you have millions of users to test on each month, you can get great precision and very narrow confidence intervals around any estimate of interest. What this thinking fails to take into account is that achieving a certain sample size, regardless of how long it takes, increases the risk of losses during testing and decreases the utility of implementing a true winner as the more users we test on, the fewer users we deploy to.

Furthermore, a 0.1% lift might not even justify the cost of the A/B test for a small website with modest amounts of revenue, however a 0.1% lift for the likes of Amazon or Google may equal hundreds of millions over a year or two. Similarly, a 0.1% decline can really damage a big business, thus a big business would require higher error guarantees. Detecting such minuscule improvements at low significance thresholds / high confidence levels requires amounts of users which become an issue even when testing on the largest of websites.

There are also those who think that having 100 million users a month means you can run most tests in a day or two. What is forgotten is that you can only test so fast before you run the risk of a greatly increased threat to the generalizability of the outcomes. Even though the sample size calculation might say you can run an A/B test in 2 days with the amount of traffic you have, external validity concerns would dictate you run the test for at least 7 days. Failure to do so is likely to take this into account will likely lead to poor predictive power.

**Conclusion**

Due to the economics of scale and the reality of statistical estimation, the smaller a business is, the less likely it is to have a positive expected ROI from A/B testing. A larger business seems to be more likely to benefit from A/B testing before implementing. It is not a coincidence that the companies investing most heavily in testing are also the biggest businesses around: when they do A/B testing right they can get more per dollar spent or risked than most others, even if both do things the same way.

While having a small number of users, sessions, or emails to test with certainly comes with disadvantages, they are not always as insurmountable as they may seem initially. For as long as testing makes business sense you can tweak different parameters to make certain trade-offs and still use A/B testing efficiently, both for estimation and for risk management.You can accept a higher uncertainty of one type or the other, you can test with sequential testing statistical methods, or use an alternative KPI when it makes sense. None of these are solutions unique to a small sample size situation as these are all things which can increase the efficiency of different kinds of tests. However, these options are more of a necessity if your sample size appears prohibitively small.

# A/B Testing with a Small Sample Size

**PS.** The easiest way to implement and the lowest chance of screwing it up is method #4: using conversion to next step as primary KPI (add to carts instead of sales, leads instead of purchases, etc.) There’s a small but acceptable risk and it generally is correlated pretty strongly with the end goal.