# Independent events

In probability and statistics, an independent event is an event that is not affected by the outcome of any other events. This is in contrast to dependent events. One example of an independent event is a coin toss. Assuming that the coin is fair and that it can only land on heads or tails, there is an equal probability (0.5) of either heads or tails occurring with each toss of the coin. It doesn't matter if the previous coin toss resulted in the coin landing on heads. There is still an equal probability of either tails or heads occurring on each subsequent toss of the coin.

## Probability of independent events

The probability of an independent event occurring, written as P(A) is calculated as:

Since a fair coin has two sides, heads or tails, there is only one way that heads or tails can occur, and since there are two possible outcomes, the probability of either occurring is equal to ½, or 50%.

Calculating the probability of multiple independent events occurring is relatively simple. Since independent events do not affect each other, the probability of two independent events occurring can be found as the product of their individual probabilities. Using probability notation, the probability of both independent events A and B occurring can be written as:

P(A and B) = P(A) × P(B)

or

P(A U B) = P(A) × P(B)

where "U" is the union symbol, and indicates that both events are occurring.

Example

What is the probability of rolling a 1 on one die and a 6 on another?

The probability of rolling either a 1 or 6 on either die is 1/6, since there is one way for the event to occur, and 6 possible outcomes. The probability of both occurring is the product of their individual probabilities, so given that P(A) is the probability of 1 occurring on a die and P(B) is the probability of 6 occurring:

P(A U B) = 1/6 × 1/6 = 1/36 = 0.278

There is approximately a 27.8% chance of rolling a 1 and a 6.