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Goodhart's law[0] should be posted above every experimentation team. The idea that "When a measure becomes a target, it ceases to be a good measure" is somethi

Goodhart's law[0] should be posted above every experimentation team. The idea that "When a measure becomes a target, it ceases to be a good measure" is something that every data scientist/statistician knows, but almost none heed in practice. The worse lead companies that I've worked for are the ones that claim to be "data driven". Countless dashboards showing various progress towards various targets without even a hint of understanding what the big picture might even be. One of the biggest insights I've had over a career working in data science is that the person solving a problem based on years of experience without any numbers backing their decisions almost always is making choices close enough to optimal that it isn't worth the extra energy to push it optimal. An example is that the person selling hot dogs at the park is probably pricing them nearly optimal. You could bring in a team of dynamic pricing experts, build a data center to mine costumer data, and I'm willing to bet a few hot dogs that difference between the model optimal price and what the hot dog vendor is selling is not enough to justify the cost of figuring out the difference. I likewise would not be surprised if the real, long run benefit of A/B testing does not justify the cost of both employee time and especially the SaaS products that help manage these processes... but let's not do that analysis because my salary depends on no one checking this. 0. https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law

placidpanda 5y ago
The main reason that I think I make good decisions in the absence of data, is because of how much I've relied on whatever data is available to inform my decisions and learn going forward. This is a huge advantage to multivariate testing as a practice/culture. As a consequence, it's often very easy for me to pick out when readouts are giving a deceptive answer (i.e. oh, the scope of this uplift is too much, we need to double check if something happened to negatively impact the control). I'm not sure I'd agree that people are often operating "close enough to optimal", but I would definitely agree that integrating experimentation is hard enough that sometimes the effort (or mistakes) you can introduce will cause more problems than you're helping. But I think this is more a function of how poor people are at the mechanics and the mindset of running experiments than that they're doing good enough pricing hot dogs. Experiments in many places are looked at for either CYA or boasting about quarterly results and not to truly learn/grow/improve.
hinkley 5y ago
Hofstadter's law is the only law that's self referential and practically all of them should be. It always takes longer than you expect, even when you take into account Hofstadter's Law. Goodhart's Law is describing a system that is dynamically stable, like a unicycle. The moment you stop moving, you fall over. And while really clever people can reduce that movement to a very small extent, possibly so small that an untrained observer no longer sees it, for most beginners and journeymen it's easier/safer if you just move a lot.

placidpanda 5y ago

The main reason that I think I make good decisions in the absence of data, is because of how much I've relied on whatever data is available to inform my decisions and learn going forward. This is a huge advantage to multivariate testing as a practice/culture. As a consequence, it's often very easy for me to pick out when readouts are giving a deceptive answer (i.e. oh, the scope of this uplift is too much, we need to double check if something happened to negatively impact the control). I'm not sure I'd agree that people are often operating "close enough to optimal", but I would definitely agree that integrating experimentation is hard enough that sometimes the effort (or mistakes) you can introduce will cause more problems than you're helping. But I think this is more a function of how poor people are at the mechanics and the mindset of running experiments than that they're doing good enough pricing hot dogs. Experiments in many places are looked at for either CYA or boasting about quarterly results and not to truly learn/grow/improve.

hinkley 5y ago

Hofstadter's law is the only law that's self referential and practically all of them should be. It always takes longer than you expect, even when you take into account Hofstadter's Law. Goodhart's Law is describing a system that is dynamically stable, like a unicycle. The moment you stop moving, you fall over. And while really clever people can reduce that movement to a very small extent, possibly so small that an untrained observer no longer sees it, for most beginners and journeymen it's easier/safer if you just move a lot.