How do you measure something that gives a different answer every time?

I had a simple-sounding question: does ChatGPT recommend this business? You'd think you just ask it. Ask ChatGPT "best personal injury law firm in NYC", see if the business is named, record yes or no. That works exactly once. Ask again an hour later and you might get a different answer. Not slightly different — potentially a completely different set of firms and a completely different set of cited sources. Which means the naive version of this measurement is worthless. You're not measuring visibility, you're sampling a distribution once and calling it a fact. This is the same problem anyone gets when they try to test an LLM-backed feature. Your normal testing instinct — same input, assert on output — just doesn't apply. So here's how I ended up designing around it, and the numbers that came out, which surprised me. The setup I wanted to compare four assistants (GPT-4o, Claude Haiku 4.5, Gemini 2.5 Flash, Perplexity Sonar, all with web search on) across 10 buyer-intent questions in one vertical. Something like: "Best personal injury law firm in New York City?" "Top immigration lawyers in Mumbai?" For each response I recorded two things: which businesses got named, and which URLs got cited. The cited sources come from each API's own citation metadata, so that part is structured — no scraping the prose. First pass, the results looked dramatic. The four assistants barely agreed on anything. Different firms, different sources, almost no overlap. Great finding. Except I couldn't publish it, because there was an obvious objection I couldn't answer: Maybe they weren't disagreeing with each other. Maybe each one was just disagreeing with itself. If a single assistant returns wildly different sources run to run, then "these four models cite different things" is a meaningless statement. You'd be measuring noise and calling it signal. The control The fix is the same idea as a control group. Measure the thing you're worried about, separately, and see if it explains your result.