Independent Events In Probability And Statistics

Independent Events

2 events in which the occurrence of one event doesn’t affect the occurrence of another event

Independent events refer to the kind of events where one occurrence has no effect on the probability of the other occurrence. In other words, if two events are independent, then the probability of one event happening does not influence or impact the probability of the other event happening.

For example, consider fair coin tosses, where the probability of getting heads is 1/2, and the probability of getting tails is also 1/2. If we toss a coin twice, the probability of getting heads on the first toss and getting heads on the second toss is (1/2) * (1/2) = 1/4. Though it’s possible to get two heads or two tails in a row, they are independent events, which means that the outcome of the first toss does not affect the outcome of the second toss.

Another example is rolling a dice. The probability of getting a 1 is 1/6, and the probability of getting a 2 is also 1/6. However, one roll has no impact on the next roll, which makes them independent events.

Independent events are essential in probability and statistics since many real-world applications involve multiple events that are not related. In such cases, understanding the probability of independent events becomes crucial in predicting the likelihood of a particular outcome.

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