n Experiment: Any course of or action with an unsure consequence.
Outcome: The result of a single trial of an experiment.
Event: A set of outcomes to which likelihood is assigned.
Sample Space: The set of all possible outcomes.
Probability: A numerical measure of the likelihood of an occasion, starting from 0 (impossible) to 1 (certain).

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n Bayesian Methods: Updating the likelihood of a hypothesis as extra evidence turns into obtainable.
Monte Carlo Simulations: Utilizing random sampling to approximate complex probabilistic systems.
Hypothesis Testing: Assessing the validity of assumptions or hypotheses about numerical information.

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n Discrete Probability Distribution: Deals with discrete random variables, the place outcomes are finite or countable (e.g., rolling a die).
Continuous Probability Distribution: For steady random variables, where outcomes form a continuum, like measuring heights or inventory prices.
Noteworthy examples embrace the Normal Distribution (bell curve) and the Uniform Distribution.

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