How our estimates work — and where they stop.

When you build a student profile, we produce an estimated probability of admission for each school. Think of it as a statistical estimate: a structured, evidence-based read of how a student’s record compares to what a given institution has historically admitted.

The model considers broad categories of input rather than any single score. Those categories are:

We do not publish the specific weights the model places on each category. Those weights are proprietary, and — more to the point — exact weights would imply a precision that admissions simply does not have. What matters is that the output is an estimate, not a measurement, and that it is built from the categories above.

We would rather under-promise than mislead. A few things we want to be unambiguous about:

• We are not affiliated with any college and cannot know or influence admissions decisions. No part of our software communicates with admissions offices.
• Probabilities are themselves uncertain. An estimate is our best read of the available evidence, not a count of a fixed reality — treat a number as a range, not a verdict.
• We deliberately avoid false precision. Admissions involves human judgment and factors no model can observe, so we present estimates plainly rather than implying more certainty than exists.
• Selective admissions are noisy by nature. Two similar applicants can see different outcomes, and our estimates describe tendencies across many cases, not the destiny of any one student.

Your college list is a portfolio, and any single admit probability only tells part of the story. Monte Carlo simulation runs your full list through many simulated admissions seasons — thousands of “what if” runs — to show the range of likely outcomes rather than a single guess.

The result helps answer practical questions: How likely is at least one admit from this list? Is the list balanced between ambitious and safer choices? What does a typical season look like, and what does a disappointing one look like?

What it does not do is predict your actual result. It cannot tell you which schools will admit you, and because it builds on the acceptance-rate estimates, it inherits their uncertainty. It is a tool for understanding risk and balance, not a forecast of your specific spring.

Families often say they want one thing and choose another. Conjoint analysis is a well-established research technique that reveals preference trade-offs from the choices you actually make — comparing options that vary across attributes like cost, location, size, and academic fit.

The output is a clearer picture of how you weigh those attributes against one another, so your list reflects your real priorities rather than first impressions. It is not a personality test, and it makes no claim about who anyone is. It simply describes the trade-offs revealed by your selections.

Our foundation is public, authoritative data — including the U.S. Department of Education’s College Scorecard and IPEDS (the Integrated Postsecondary Education Data System).

On top of that, our research agents continuously gather and verify information from primary sources, such as institutions’ own published figures. Every data point is source-tracked, so the information behind an estimate can be traced back to where it came from. Published figures change over time, and there can be lags between an institution’s update and ours.