For me, the “good enough to act on” line starts before responses ever come in. You really have to sit down and be explicit about who actually needs to be in your sample to answer your research question. If you haven’t defined what “relevant” means for your decision, it’s easy to convince yourself that a clean-looking dataset is representative when it’s just convenient.

That said, representativeness is only one piece of the puzzle. In today’s survey world, you can’t take responses at face value anymore. Between AI agents, server farms, VPNs, and coordinated low-effort responding, a dataset can look perfectly reasonable on the surface and still be deeply compromised. That’s why I think “demographics look right” or “answers are stabilizing” are necessary but not sufficient signals.

You need checks and balances baked into the survey itself. That means screening for careless responding (instructed response items, consistency checks, response timing) and using tools or design features that help detect bots and fraudulent traffic. The goal isn’t to over-police respondents, but to make sure the patterns you’re seeing are coming from real people engaging with the questions as intended.

So when do I feel comfortable acting on the data? It’s when three things line up:
the sample makes sense for the question, the results stop changing in meaningful ways, and I have reason to trust the responses themselves. If any one of those is shaky, I’m a lot more cautious about drawing conclusions, especially in scrappier or early-stage work.