[size=150]1. Sample Space: The set of all possible outcomes of a random experiment.[br][br]2. Event: A subset of the sample space, representing a specific outcome or a collection of outcomes.[br][br]3. Probability: A measure of the likelihood of an event occurring, typically represented as a number between 0 and 1.[br][br]4. Probability Function: A function that assigns probabilities to events in the sample space, satisfying certain properties (such as non-negativity and additivity).[br][br]5. Random Variable: A variable that takes on values determined by the outcome of a random experiment.[br][br]6. Probability Distribution: The set of all possible values of a random variable and their corresponding probabilities.[br][br]7. Joint Probability: The probability of the simultaneous occurrence of two or more events.[br][br]8. Conditional Probability: The probability of an event given that another event has occurred.[br][br]9. Independence: Two events are independent if the occurrence or non-occurrence of one does not affect the probability of the other.[br][br]10. Complementary Event: The event consisting of all outcomes that are not in a given event.[br][br]11. Union of Events: The event that consists of outcomes that belong to either or both of two events.[br][br]12. Intersection of Events: The event that consists of outcomes that belong to both of two events.[br][br]13. Mutually Exclusive Events: Two events are mutually exclusive if they cannot occur simultaneously.[br][br]14. Counting Techniques: Methods used to count the number of outcomes in a sample space, such as permutations and combinations.[br][br]15. Expected Value: The average value of a random variable, weighted by their probabilities.[br][br]16. Variance: A measure of the spread or dispersion of a random variable around its expected value.[br][br]17. Bernoulli Trial: A random experiment with two possible outcomes, usually referred to as success and failure.[br][br]18. Binomial Distribution: A probability distribution that models the number of successes in a fixed number of independent Bernoulli trials.[br][br]19. Geometric Distribution: A probability distribution that models the number of trials needed to achieve the first success in a sequence of independent Bernoulli trials.[br][br]20. Poisson Distribution: A probability distribution that models the number of events occurring in a fixed interval of time or space, under certain assumptions.[/size]