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Independent probability is the probability of two or more events occurring together, where the occurrence of one event does not affect the probability of the other event(s).

In probability theory, two events are said to be independent if the occurrence of one event does not affect the probability of the other event(s). For example, if we toss a coin twice, the probability of getting heads on the first toss is 1/2, and the probability of getting heads on the second toss is also 1/2. The probability of getting heads on both tosses is the product of the individual probabilities, i.e., (1/2) x (1/2) = 1/4. This is an example of independent probability.

To determine whether two events are independent, we can use the formula P(A and B) = P(A) x P(B), where P(A and B) is the probability of both events occurring together, P(A) is the probability of event A occurring, and P(B) is the probability of event B occurring. If the formula holds true, then the events are independent.

Independent probability is important in many areas of mathematics and statistics, including hypothesis testing, regression analysis, and machine learning. It allows us to make predictions and draw conclusions based on data, without assuming that one event affects the probability of another event.

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