Expectation
A probability-weighted average describing long-run center.
Probability & Data · Accessible first encounter
Probability theory provides a mathematical language for uncertain events, random variables, expectation, dependence, and long-run behavior. It supports statistics, finance, science, algorithms, and decision-making.
01 · Opening mystery
In the Monty Hall problem, you choose one of three doors. The host, who knows where the prize is, opens a different door showing no prize. Two doors remain, so it feels as if each should have probability 1/2.
The host’s action is not random ignorance; it carries information. Your original choice still had probability 1/3, while the unchosen pair collectively had probability 2/3. The reveal concentrates that 2/3 on the remaining alternative.
Make a prediction. The laboratory is designed to challenge or refine it.
02 · Interactive laboratory
Run fresh Monty Hall simulations. Small runs fluctuate; large runs settle near the theoretical probabilities 1/3 and 2/3.
03 · The big idea
The conditional probability P(A | B) asks for the probability of A within the reduced world where B has occurred. Its formula divides the probability of both A and B by the probability of B.
This framework distinguishes dependence from independence. Events A and B are independent when learning B does not change the probability of A.
Conditional probability is the probability of A after restricting attention to outcomes in B.
A probability-weighted average describing long-run center.
Learning one event does not change the probability of the other.
Combines prior belief with evidence to produce a posterior probability.
04 · A beautiful result
Often we know how likely evidence is under a proposed cause, P(E | C), but want the probability of the cause after seeing the evidence, P(C | E). Bayes’ rule connects the two.
The prior P(C) matters. A highly accurate medical test can still produce many false alarms when the condition is very rare because most tested people begin in the no-condition group.
By the conditional formula, P(C | E) = P(C ∩ E)/P(E).
Also P(E | C) = P(C ∩ E)/P(C).
Therefore P(C ∩ E) = P(E | C)P(C).
Substitute to obtain P(C | E) = P(E | C)P(C)/P(E).
05 · Why this subject matters
Probability models repeated experiments, measurement noise, reliability, queues, genetics, insurance claims, market risk, and randomized algorithms. A mathematical model states what is random and how outcomes are weighted.
Advanced probability studies random variables, distributions, convergence, stochastic processes, martingales, and measure-theoretic foundations.
Learns about populations from uncertain samples.
Describes uncertain returns and contingent payoffs.
Models systems evolving through random transitions.
06 · Friendly assessment
The questions focus on the main insights, not obscure details. Each response receives an explanation immediately.
Where this idea leads
Turn probability models into inference from data.
Explore →Connected fieldStudy random behavior through time.
Explore →Connected fieldPool and price uncertain future claims.
Explore →Connected fieldValue and manage uncertain financial payoffs.
Explore →This is an invitation to continue, not a compressed substitute for a full university course.