Definitions for

**"Independent events"****Related Terms:**Dependent events, Conditional probability, Joint probability, Probability, Event, Independence, Theoretical probability, Compound event, Posterior probability, Likelihood, Uncertainty, Prior probability, Bayes' theorem, Statistical significance, Probability theory, Bernoulli trial, Statistically significant, Significant, Binomial distribution, Predict, Variability, P-value, Probability distribution, Significance, Likely, Poisson distribution, P value, Assumption, Coincidence, Random, Uncertainty, Experimental probability, Likelihood function, Expectation, Prediction, Deterministic, Frequency, Chance, Indicator, Level of significance, Chi-square, Likelihood ratio, Law of large numbers, Odds ratio, Prognostic, Event group, Forecast, Stochastic, Markov chain

Events are independent when the occurrence of one has no effect on the probability of the occurrence of the other.

Two events in which the outcome of the second is not affected by the outcome of the first.

events such that the outcome of one has no effect on the probability of the outcome of the other.

the idea that two events do not connect with each other in any observable pattern, and hence that neither event can give any useful information about the other event. In contrast, if two events are not independent, they are said to associated. Numerical variables that are not independent are said to be correlated.

Events in which the outcome of one event does not affect the outcome of the other event.

Events such that the outcome of the first event has no effect on the probabilities of the outcome of the second event. (e.g., two tosses of the same coin are independent events).

When the outcome of one event has no bearing or effect on the outcome of another event.

two events whose outcomes have no effect on one another. For example, the outcome of the second flip of a coin is independent of the first flip of a coin.

Two events are independent if the occurrence (or non-occurrence) of one event has no affect on the probability of the other event occurring.

Events for which one outcome does not affect another outcome.

Are events where knowing the outcome of one does not affect the probability of the other. An example is two separate coin tosses. The first toss resulting in a heads situation does not effect the probability of the second toss.

Events whose occurrence or outcome has no effect on the probability of each other.

Two events A and B are independent if the probability that they happen at the same time is the product of the probabilities that each occurs individually; i.e., if P(A & B) = P(A)P(B). In other words, learning that one event occurs does not give any information about whether the other event occurred too: the conditional probability of A given B is the same as the unconditional probability of A, i.e., P(A/B) = P(A).

A property of two events and if their probabilities satisfy P(AB) = P(A) P(B).

Two or more events in which the outcome of one event has no effect on the outcome of the other event or events.

Two events in which the outcome of the first event does not affect the outcome of the second event.

Two events not affected by each other. Two events A and B are independent if and only if P(A and then B) P(A) x P(B).