The most used distributions to describe real world data examples in statistical analysis.

Here are some of the most used distributions with real world examples :

1- Uniform distribution.
It is used to distribute probability equally over all
possible outcomes (discrete) or equal ranges of outcomes (continuous),
this distribution is especially useful in virtual experiments or simulations to explore real‐world phenomena.

2- Binomial distribution.
You can use it to model the number of successes that can occur
in a certain number of attempts when only two outcomes are possible
( heads‐or‐tails coin‐flip, for example).

3-Poisson distribution.
The Poisson (discrete) and exponential (continuous) distributions complement one another. Say
that there is a cross road in your area that has a lot of accidents. A Poisson distribution answers the question, “What is the probability that such‐and‐such number of accidents will occur there within a month?” And
an exponential distribution answers the question, “What is the probability that the time until the next accident is such‐and‐such length of time?”

4-Normal distribution . The most famous distribution with a bell curve . These distributions model phenomena that tend toward some most likely value (the top of the bell in the bell curve) with values at the two extremes becoming less likely.

5- Gama distribution for predicting time-to failure of a machinery or device in an industrial context.