We Do Passage — One Thing at a Time

You already know the rule for a fair test. Change one thing. Hold everything else still. If you change two things and the result moves, you cannot say which change moved it.

The rule is easy to state and hard to follow, and it gets harder when the thing you are changing is words.

Start with the machine. A model does not receive your sentence. It receives tokens, and the split into tokens happens before any prediction is made. So when you swap a word in your prompt, you are not making one small edit to a sentence. You may be changing how many tokens go in, where they break, and therefore what the model has in front of it at every single step of the loop that follows. A change that looks tiny on your screen is not necessarily tiny inside the machine.

Now add the second problem. Even with the prompt held perfectly still, the answer can move on its own, because the picking has chance in it. So a researcher comparing two models is trying to see a difference through two layers of noise at once: the noise of wording and the noise of chance.

This is not a problem that machines invented. It is the oldest problem in survey research, and the people who do that work for a living are careful about it in a way that is worth copying.

Consider how the Pew Research Center handles it. Pew has tracked American use of AI chatbots for several years running, surveying 5,119 adults in February 2026 for its most recent report (Pew Research Center, 2026). Tracking something across years means asking the same question the same way every time, so that a change in the answers means a change in the country and not a change in the questionnaire.

But Pew needed to change a question. Through 2025 they had asked people whether they had ever used ChatGPT. By 2026, ChatGPT was no longer the only chatbot worth asking about, so the question was widened to cover others as well (Pew Research Center, 2026). That is a better question. It is also a different question, which means the number it produces cannot be lined up against the older numbers as though nothing happened.

Pew’s response is the part to notice. They did not quietly swap the wording and let the line on the graph keep going. They broke the line. On the published chart, the segment covering the wording change is drawn as a dotted line rather than a solid one, and the note under the chart states plainly what changed and when (Pew Research Center, 2026). The reader is told, at the exact point where comparison stops being safe, that comparison has stopped being safe.

That is the standard. Not never change anything. Change what you must, then mark the break so nobody reads across it by accident.

Pew applies the same care when the population changes. Its survey of teenagers reached 1,458 respondents ages 13 to 17, recruited through their parents, and the report is explicit that these findings describe teenagers and not adults (Pew Research Center, 2025). Two numbers from two different populations are not a trend. They are two numbers.

Bring this back to your station. Next week, seven groups will send one prompt to three models. Held constant: the words, the order, the moment. Changing: which model receives them. That is the design, and it is only worth anything if the constant part really does stay constant.

Which raises the question you have to answer in a minute. If your group decides on Thursday to change one thing about the prompt, what counts as one thing? A single word? A whole sentence? The order of two sentences? There is no automatic answer here. There is only the answer your group writes down in advance and then holds itself to, and the honesty to mark the break when you cross it.

References

Pew Research Center. (2025, December 9). Teens, social media and AI chatbots 2025. https://www.pewresearch.org/internet/2025/12/09/teens-social-media-and-ai-chatbots-2025/

Pew Research Center. (2026, June 17). Americans and AI 2026: Chatbots, smart devices and views on impact. https://www.pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/

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