Why statistical honesty beats a confident guess

Every analytics tool is happy to give you a number. Type in a campaign, and out comes a ROAS, a conversion rate, a forecast — clean, confident, and ready to bet a budget on. The problem is that a number is not the same as evidence.

A confident number is not a reliable one

If a campaign has driven three conversions out of a hundred clicks, the "conversion rate" is 3%. But the honest version of that number is closer to "somewhere between 1% and 7%, and we really can't say yet." A tool that prints 3% and stops has hidden the only thing that matters: how much you should trust it.

Why "insufficient signal" is a feature

ROIS treats uncertainty as a first-class result. When the data is too thin to support a call, it says so — out loud — instead of inventing a precise-looking number. That sounds modest, but it's the difference between spending on a hunch dressed up as data and waiting one more week for the signal to arrive.

When do you have enough?

There's no single magic sample size. It depends on how far your observed numbers sit from the break-even point and how wide the uncertainty still is. The practical rule: the more a decision hinges on small differences, the more data you need before the difference is real. ROIS makes that threshold explicit rather than leaving it to gut feel.

Honesty isn't the opposite of useful. For a small budget, it is the useful part.