IA ideas library

Probability IA ideas: chance, risk and expected value

Probability explorations can be done with no data collection at all — the mathematics is the investigation. You set up a model of a random process, calculate probabilities or expected values exactly, then check the model with a simulation or real results.

The strongest probability IAs ask a question someone would actually care about: is this game fair, how many attempts will it take, is this strategy better than that one?

Next step: shortlist ideas with the free IA topic explorer, which filters starting points by course, interest and how easy the data is to get. For feedback on your own draft, the IA package (€39 once) gives you 5 AI feedback credits and every annotated exemplar in full.

How probability explorations reach the top bands

Personal engagement (C)

Play, simulate or collect the game or situation yourself. Predicting the answer before calculating, then checking it with an experiment, is engagement an examiner can see.

Reflection (D)

Compare theory with simulation or real data and explain differences. Question the assumptions of independence and equal likelihood.

Use of mathematics (E)

SL: A probability model built step by step (tree diagrams, binomial or normal distributions, expected value), used correctly, with the result checked by simulation or data.

HL: HL probability such as Bayes' theorem, Poisson processes, Markov chains, continuous random variables or a general result proved for n, not just computed for one case.

An HL version

Generalise the result to n (players, doors, dice) and prove it, or model the process as a Markov chain or with a continuous distribution.

Probability & chance ideas (13)

Also relevant (42)

Frequently asked questions

Can a probability IA get top marks without any data?

Yes. A theoretical investigation can score full marks if the question is clear, the reasoning is correct and well explained, and you reflect on the assumptions. Simulations or small experiments make the reflection much easier.

Is the birthday problem too simple for an IA?

On its own, yes. It becomes a good IA if you extend it — for example to near-matches (birthdays within a day), unequal birth rates across the year, or the expected group size for the first match.

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Related types of IA: Simulation & Monte Carlo methods

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