One of the most important ideas in IB Maths AI is that no model is ever fully reliable. This can feel uncomfortable for students, especially after spending time building or applying a model carefully. However, IB is not testing whether a model works perfectly — it is testing whether students understand why perfection is impossible.
All models are simplifications. They reduce complex, real-world situations into manageable mathematical forms by making assumptions. These assumptions might include constant rates, linear relationships, independence, or normality. Even when these assumptions are reasonable, they are never completely true. This gap between reality and the model is unavoidable.
Another reason models are unreliable is incomplete information. Data is almost always limited by sample size, measurement error, or bias. A model built on imperfect data inherits those imperfections. IB expects students to recognise that conclusions are constrained by the quality and scope of the data used.
Models are also sensitive to changing conditions. A model that fits past data well may fail when circumstances shift. Economic trends, human behaviour, environmental factors, or technology can all change relationships between variables. This is why IB often penalises strong predictions made without caution.
Students sometimes believe that improving a model mathematically removes unreliability. While refinement can help, it never eliminates uncertainty. Adding complexity often introduces new assumptions and new sources of error. IB values awareness of this trade-off far more than technical sophistication.
IB includes questions about model reliability to encourage responsible interpretation. Students who acknowledge uncertainty, limit conclusions appropriately, and explain where a model may fail demonstrate strong analytical thinking. Students who claim certainty usually lose marks, even if their calculations are correct.
Importantly, unreliability does not make models useless. Models are valuable tools for understanding patterns, comparing options, and guiding decisions. IB wants students to recognise that usefulness and reliability are not the same thing.
Once students accept that every model has limits, evaluation questions become clearer. Instead of defending the model, students explain how much trust it deserves — and why.
In IB Maths AI, recognising uncertainty is not a weakness. It is evidence of understanding.
Frequently Asked Questions
Does an unreliable model mean it’s wrong?
No. It can still be useful, as long as its limitations are understood.
Should I always mention unreliability?
Yes, especially in modelling, prediction, or evaluation questions.
How much should I say about limitations?
One or two clear, relevant points are usually enough.
RevisionDojo Call to Action
IB Maths AI rewards students who understand the limits of mathematics, not those who ignore them. RevisionDojo is the best platform for IB Maths AI because it trains students to evaluate model reliability, explain uncertainty clearly, and earn interpretation marks consistently. If modelling questions still feel risky, RevisionDojo helps you turn limitations into guaranteed marks.
