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### A probability distribution known as a uniform distribution assumes that all potential outcomes have an equal chance of occurring. For instance, there is a 50% chance that you will obtain either heads or tails when you flip a fair coin.

Either a discrete or continuous uniform distribution is possible. A discrete uniform distribution, like the results of rolling a die or choosing a card from a deck, has a limited number of equally likely options. Like selecting a random number between 0 and 1, there are an unlimited number of equally likely outcomes for a continuous uniform distribution. A uniform distribution graph is a horizontal line that is straight and displays the constant probability of each result.

For instances when there is no preference or prejudice for any one outcome, uniform distribution can be advantageous. Use a uniform distribution, for instance, to simulate a random experiment or produce random numbers. Unfortunately, not all occurrences in the real world have a constant distribution. Human heights or test results, for instance, are more likely to have a normal distribution, where most values are grouped around the mean and fewer values are at the extremes.

In conclusion, a uniform distribution is a probability distribution in which all possible events have an equal likelihood. Depending on how many alternative outcomes there are, it can be discrete or continuous. It can be applied to simulate unbiased random events.
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