Statistics turns repeated data into a model
In Grade 11 statistics, students move from single probability questions into models that describe many trials. A model is useful only when its assumptions match the situation.
Probability distributions organize possible results
A probability distribution lists what can happen and how likely each result is. Joint probability measures overlap between events, the binomial distribution handles repeated yes-or-no trials, the Poisson distribution models event counts in fixed intervals, the normal distribution models smooth bell-shaped data, a cumulative distribution function adds probability up to a cutoff value, and a conditional probability distribution shows how probabilities change after information is known.
Start with distributions, CDFs, and conditions
The joint probability lesson explains event overlap and two-way tables. The binomial distributions lesson explains repeated-trial probability. The Poisson distribution lesson explains count models, lambda, and rare-event probability. The normal probability distribution lesson explains bell curves, z-scores, and area as probability. The cumulative distribution function lesson explains at-most probabilities, interval probabilities, percentiles, and CDF graphs. The conditional probability distribution lesson explains how a distribution changes after a condition is given.