Editing Probability Theory (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Probability''' — A measure of the likelihood that an event will occur, ranging from 0 (impossible) to 1 (certain).
* '''Sample Space ($S$)''' — The set of all possible outcomes of an experiment.
* '''Event ($E$)''' — A subset of the sample space (e.g., "Rolling an even number").
* '''Independent Events''' — Events where the outcome of one does not affect the other (e.g., two coin flips).
* '''Dependent Events''' — Events where the outcome of one affects the likelihood of the other (e.g., drawing cards without replacement).
* '''Conditional Probability''' — The probability of an event occurring given that another event has already occurred ($P(A|B)$).
* '''Bayes' Theorem''' — A formula that describes how to update the probability of a hypothesis as more evidence becomes available.
* '''Random Variable''' — A variable whose value is determined by the outcome of a random experiment.
* '''Mean (Expected Value)''' — The "Average" outcome if an experiment is repeated infinitely.
* '''Variance / Standard Deviation''' — Measures of how "spread out" the outcomes are from the mean.
* '''Normal Distribution (Bell Curve)''' — A common probability distribution where most outcomes cluster around the center.
* '''Law of Large Numbers''' — The principle that as a sample size grows, its mean gets closer to the average of the whole population.
* '''Central Limit Theorem''' — The amazing fact that the sum of many independent random variables tends toward a normal distribution, regardless of the original distribution.
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