Probability and Statistics Lab
Investigate binomial distributions and sampling behaviour.
Beauty in maths topic
Statistics, probability and AI ask how we reason from uncertainty, data and repeated experiments.
6
live pages
6
prototype tools
6
planned ideas
Sampling, distributions and hypothesis testing
Monte Carlo simulation and random walks
Regression, overfitting and model choice
Optimisation and neural-network ideas
What does the data support, and what does it not support?
How can randomness produce reliable patterns?
When does a model start learning noise instead of structure?
These are the pages currently available to open from this topic.
Investigate binomial distributions and sampling behaviour.
See rejection regions, z statistics, p-values and conclusions.
Estimate pi and area under a curve using random sampling.
Compare training error, test error and model complexity.
Watch optimisation move across a loss landscape.
Build two-stage probability trees, compare replacement, and highlight combined events.
These ideas are not built yet, but they show where this topic could grow next.
Planned lab linking probability, matrices and long-run behaviour in systems that move between states.
Planned AI visualisation connecting functions, optimisation, data and model training.
Planned bridge from simple probability to diffusion, finance, physics and wandering paths.
Planned guide for binomial and normal tests, critical regions, p-values and careful conclusion writing.
Planned page asking students to choose, test and critique modelling assumptions in mechanics and statistics.
Planned page about one-way functions, checksums, tamper detection and avalanche effects.
A portal for connected mathematical explorations: pattern, surprise, structure, nature and emergence.
Codes, ciphers, primes, modular arithmetic, public keys and secure communication.
Strategy, cooperation, competition, voting, incentives and mathematical decision making.
Entropy, compression, communication, error correction and the mathematics of messages.
Growth, uncertainty, modelling, risk, portfolios and the mathematics behind financial decisions.
Clustering, dimensions, similarity, projection and the hidden shape of high-dimensional data.
This area is increasingly important because it connects school mathematics to evidence, modelling and AI literacy.