adScientist is the autonomous test bench for paid social. Bulk-generate creative with AI, run them through Rapid Split Testing — auto-pause at 2,000 impressions, promote winners under $1.00 CPOLC, and let the rule engine kill what doesn’t earn its keep. No dashboards to babysit. No ad fatigue you missed at midnight.
The conventional wisdom says you need 7,000–10,000 impressions to be 95% confident a variant is a winner. That answer is correct — for the question "did this beat the control?" But that’s a research question. You don’t have a research question. You have a business: is this ad earning its keep, today? Different question. Different threshold. Different math.
That’s a research question. The right tool is statistical significance, the right threshold is ~95% confidence, and the right sample size is 7K–10K impressions.
Useful for academic papers. Catastrophic for a $100/wk ad budget — at that spend, the standard method gives you one swing per week. That’s not a testing program. That’s a prayer.
That’s a business question. The right tool isn’t a confidence interval — it’s a performance gate. The right threshold is whatever proves the ad pays its rent.
Run five variants where the textbook runs one. Each gets 2,000 impressions. The ones that clear the gate get promoted. The ones that don’t get killed before they bleed you dry.
A variant that produces five real outbound clicks at under a dollar each has demonstrated something more valuable than statistical separation from baseline: it has demonstrated economic viability.
The gate does the work the impression count would otherwise do. We don’t need a research paper. We need a winner. The 2,000-impression auto-pause isn’t a corner cut — it’s a portfolio optimization made possible because the gate is doing the heavy lifting.
Meta is asking “is this statistically true?”
You should be asking “is this profitable?”
They are not the same question.