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Comment by err4nt | Hacker News Reader

The purpose of an A/B test isn't to always show the best performing result, it's to perform a _controlled scientific experiment_ with a control group, from which you can learn things.

Also, I work in this field and I will just say that people _do_ behave differently based on traffic source: i.e. users coming from Facebook behave alike, but different than traffic from Reddit who act similarly to each other. If you were running a self-optimizing thing like this it _would_ make sense to split it up by the different traffic sources and handle them separately.

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What you said is correct, but I'd like to point out that controlled scientific experiments may not be the right approach for e.g. optimizing conversions on a website.

The reason is that websites are a dynamic environment. All things equal, better controlled experiments are great. However, visitor behavior, especially when from dynamic sources (google serps change weekly), changes all the time.

And that's why I prefer MAB over A/B tests -- A/B tests don't adapt to a dynamic system, so we often wish away the changes in the system to use it. Does anyone go back and re-test their biggest wins?

> Does anyone go back and re-test their biggest wins?

Yes, absolutely! We do research first, then come up with simple, well-controlled tests. Once we have a winner we can either lock it in, but often we continue to research and experiment on the new knowledge we gained. A hefty minority of the tests I implement build on past wins to further flesh out what works and what doesn't with knowledge and the data to back it up.

How do you do knowledge transfer when you're documenting what you've learned? I have found even simple changes often don't reproduce on the same site. It feels like a easy trap for an expert practitioner to fall into -- the belief they can predict the outcome of a test based on a different test they ran.

Sometimes, yes, but often it's not for the reasons people think. This seems very much in Danny Kahneman land -- we can't trust what our brain is telling us, that this experience will have similar results in this other context.

I recently worked at a place that ran > 20 websites, huge traffic numbers, with around 100 simultaneous tests. We would poll each employee about their guess for the test winner. The results were about on par with everyone randomly guessing.

Very interesting. It seems to me that doing incremental work like this might end up in a local minima/maxima. Do you have any advice on how to avoid pitfalls like that? Are you testing radically different ideas along with your incremental improvements?

From a workflow perspective, MAB is a bit difficult to find radical improvements from. The radical improvements come from fundamental design changes, of which it would be very expensive to create a bunch of radically different variants.

MAB is best used where you can generate a bunch of variants cheaply and hope for a 30% gain.

Cool, thanks.

Yes. Bandits will often converge more quickly to the optimal strategy, but it is much more difficult to understand why that strategy is optimal and generalize from the bandit outcomes to predict future performance and performance of other strategies.

It isn't impossible - bandits are seeing adoption in medical trials to avoid precisely the problem discussed - but the standard experiment design and analysis techniques you learn in a decent college statistics class or introductory statistics text no longer apply. That's one of the beauties of A/B testing: while it does require substantial thought to do well, the basic statistics of the setup are very well-understood at this point.

But for results to generalize or to understand why, the confounders must be accounted for in the randomization. This is really hard to do well -- there are often subtle influences that aren't sufficiently understood how they impact these non-linear systems. What makes someone convert? A million different factors; changing the color of a button in one context doesn't necessarily tell me much about how people would respond to that experience in another context.

It's easy to underestimate how complex things are, because we only see some superficial aspects of e.g. a user/software interaction model. This flaw is down to how our brains work -- ref "What you see is all there is".

I disagree. I’ve spent a lot of time staring at bandit outcomes and usually they match some sort of intuition of why a variant might be exceptional.

That could be post-hoc reasoning, though. It would be interesting to pre-register your hypotheses, or see whether you could tell bandit outcomes from random ones.

Sure it’s post-hoc reasoning, but it doesn’t matter because I’m not trying to invalidate a hypothesis.

I’m looking for variants that win. When I find one that wins I look at it and try to add more of the same flavor to the product.

This process works.

This is literally the logical fallacy. You could get lucky. Maybe you have obvious gains to chase. But bad logical arguments are bad because they never work forever. They are corrupted heuristics that can get you in trouble without critical thinking.

Edit: added in forever. Phone dropped some wording I originally had. I think.

Call it a genetic algorithm if you like. I’m looking for incremental wins in a world of infinite possibilities, not truth.

Incremental wins can still lead to dead ends. My phrasing was off in my post. I meant to say that the fallacies aren't that the tactics never work. Just that they can stop working without you really realizing it. A heuristics that can lead you down a dead end.

By all means, keep doing it if it is working for you. But don't confuse it as good advice. And stay vigilant.

Products exist in human reality not some science paper. There are no absolute truths, everything dead-ends eventually. It’s like trying to prove that one set of genes is better than another for future survival - an impossible task.

This belies a belief that science doesn't reflect the real world. It absolutely does.

Again, it may be working in your case. Argument to authority can go a long way. Even ad hom attacks often exist due to a "smell" of the person speaking. It is not, however, logically sound and can easily lead to unsupportable positions.

So, take care. And realize that a lot of the damage of poor practices may be tangential. For example, a belief that the real world can not be described by science.

Isn't this problem also an issue when people talk about transferring what they learn from one test to another test? That is frequently cited as a benefit of A/B testing.

> The purpose of an A/B test isn't to always show the best performing result

The key part is 'not always'.

Using A/B tests just to find the better performing version is a valid use case too. And if you have the traffic, multi-armed bandits are a nice way of automating the whole procedure. Even scientifically speaking there is nothing wrong with them. Their biggest issue is that they require a lot of traffic for significant results.

In practice, MAB should require less traffic than an A/B test for the same level of significance. Although it's much more difficult to rigorously describe that significance level with a MAB.

Wouldn't a simple solution be to create a handler that runs a MAB for each traffic source?

If you're worried about the site changing for people between visits from different traffic sources, you can cookie their MAB/test-assignment on the first visit.

> Also, I work in this field and I will just say that people _do_ behave differently based on traffic source: i.e. users coming from Facebook behave alike, but different than traffic from Reddit who act similarly to each other.

Out of curiosity, what is your hypothesis for explaining this difference in behavior? Would you say it's primarily due to differing contexts in which a link is posted, or differing populations on each platform? Or maybe these both contribute about equally?

Stated another way: would you expect the same individual to behave differently coming from Facebook versus coming from reddit, if they happen to be a user of both?

Different demographics, different intent, and different mental context all play a factor.

Not a statistician by any means, but could traffic source be a factor that's evaluated alongside conversion by a bandit algorithm when calculating the chance to show a particular option? Or other factors as well (detected device capabilities, users location, etc?)

These could just be weighting factors so instead of a single % chance per option, every time there's a successful interaction the victory is spread across the factors for that option.

New users could be shown the option where the chance for each option is weighted by the factors they match. Is this a valid approach or would it introduce some kind of selection bias?

Yes, of course. You can use a contextual MAB and include the traffic source in the context. Contextual MABs are much more complicated and expensive to implement though.

> The purpose of an A/B test isn't to always show the best performing result, it's to perform a _controlled scientific experiment_ with a control group, from which you can learn things.

Occasionally there is pure scientific interest.

But far more frequently, the purpose of the A/B is to optimize the outcome.

This is why Google Analytics has exclusively chosen multi-armed bandit for its its A/B test framework.

It doesn't have to be a pure science interest.

If you want to use the results from one test to inform what to test next, then A/B tests are better optimizing for truth.

Most website changes dont make a significant difference in conversion. If you use MAB, then you dont know if the winner is really better or the result of random variation.

> If you use MAB, then you dont know if the winner is really better or the result of random variation.

No, you know the outcome of MAB is the best.

If it's clearly the best, it converges quickly. If it's less clearly the best, it converges slowly. Either way though, you haven't lost any more conversions that necessary to find that out, or needed to put in non-mathematically fail safes.

That’s just a contextual multi-armed bandit.

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