Comment by xp84

Comment by xp84 5 days ago

9 replies

> politically derisks the change, by tying it's deployment to rigorous testing that proves it at least does no harm to the existing process before applying it to all users.

I just want to drop here the anecdata that I've worked for a total of about 10 years in startups that proudly call themselves "data-driven" and which worshipped "A/B testing." One of them hired a data science team which actually did some decently rigorous analysis on our tests and advised things like when we had achieved statistical significance, how many impressions we needed to have, etc. The other did not and just had someone looking at very simple comparisons in Optimizely.

In both cases, the influential management people who ultimately owned the decisions would simply rig every "test" to fit the story they already believed, by doing things like running the test until the results looked "positive" but not until it was statistically significant. Or, by measuring several metrics and deciding later on to make the decision based on whichever one was positive [at the time]. Or, by skipping testing entirely and saying we'd just "used a pre/post comparison" to prove it out. Or even by just dismissing a 'failure,' saying we would do it anyway because it's foundational to X, Y, and Z which really will improve (insert metric) The funny part is that none of these people thought they were playing dirty, they believed that they were making their decisions scientifically!

Basically, I suspect a lot of small and medium companies say they do "A/B testing" and are "data-driven" when really they're just using slightly fancy feature flags and relying on some director's gut feelings.

mikepurvis 5 days ago

At a small enough scale, gut feelings can be totally reasonable; taste is important and I'd rather follow an opinionated leader with good taste than someone who sits on their hands waiting for "the data". Anyway, your investors want you to move quickly because they're A/B testing you for surviveability against everything else in their portfolio.

The worst is surely when management make the investments in rigor but then still ignores the guidance and goes with their gut feelings that were available all along.

  • legendofbrando 5 days ago

    Huge plus one to this. We undervalue when to bet on data and when to be comfortable with gut.

weitendorf 5 days ago

I think your management was acting more competently than you are giving them credit for.

If A/B testing data is weak or inconclusively, and you’re at a startup with time/financial pressure, I’m sure it’s almost always better to just make a decision and move on than to spend even more time on analysis and waiting to achieve some fixed level of statistical power. It would be a complete waste of time for a company with limited manpower that needs to grow 30% per year to chase after marginal improvements.

  • zelphirkalt 5 days ago

    One shouldn't claim to be "data-driven", when one doesn't have a clue what that means. Just admit, that you will follow the leader's gut feeling at this company then.

    • closewith 5 days ago

      In all cases, data-driven means we establish context for our gut decisions. In the end, it's always a judgement call.

      • khafra 5 days ago

        Robin Hanson recently related the story of a firm which actually made data-driven decisions, back in the early 80's: https://www.overcomingbias.com/p/hail-jeffrey-wernick

        It went really well, and then nobody ever tried it again.

        • closewith 5 days ago

          That's a counter-example. The prediction markets were used to inform gut feelings, not controlling the company, and in the end, when he wanted to stop, he stopped following the markets and shut the company.

petesergeant 5 days ago

> Basically, I suspect a lot of small and medium companies say they do "A/B testing" and are "data-driven" when really they're just using slightly fancy feature flags and relying on some director's gut feelings.

see also Scrum and Agile. Or continuous deployment. Or anything else that's hard to do well, and easier to just cargo-cult some results on and call it done.

DanielHB 5 days ago

I worked at an almost-medium-sized company and we did quite a lot of A/B testing. In most cases the data people would be like "no meaningful difference in user behaviour". Going by gut feeling and overall product metrics (like user churn) turns out to be pretty okay most of the time.

The one place that A/B testing seem to have a huge impact was on the acquisition flow and onboarding, but not in the actual product per se.