WHAT IS AN EXAMPLE OF DISCRETE DISTRIBUTION AND CONTINUOUS DISTRIBUTIONS?

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An example of a discrete probability distribution is the binomial distribution, where the outcomes are discrete and can only take on a finite or countably infinite set of values. For instance, flipping a coin multiple times and counting the number of heads would follow a binomial distribution.

To calculate the probability of a specific outcome using a discrete distribution, you can use the probability mass function (PMF). The PMF gives the probability of each possible outcome.

On the other hand, a continuous probability distribution describes variables that can take on any value within a certain range or interval. An example of a continuous distribution is the normal distribution (also known as the Gaussian distribution). It is commonly used to model real-world phenomena like heights, weights, or errors in measurements.

To calculate the probability of a specific range of values using a continuous distribution, you can use the probability density function (PDF). The PDF represents the relative likelihood of different values.

In summary, discrete distributions deal with data that has specific, separate values, while continuous distributions describe data that can take on any value within a range.