Probability distributions are basically used to do future analysis or predictions. This is basically dependent on mathematical formulas. There is a list of probability distributions, which have their own significance in real-life applications. For example, Binomial distributions are used to predict two outcomes of any event which are usually described as Success and Failure. In the same way, the Bernoulli distribution gives only two possible outcomes, Yes or No.

Likewise, there are many other distributions, which are used in regular life. One of the basic distributions is uniform distribution. In this distribution, all values between two boundaries happen roughly evenly. Suppose, if we roll a six-sided die, we are equally likely to get 1, 2, 3, 4, 5, or 6. Now, if we roll the die 3,000 times, we would probably get roughly 500 of each result. Therefore, the results would form a uniform distribution from 1 to 6.

Poisson distributions give the probability of something happening a specific number of times if it typically happens at a fixed pace and each event is independent of previous events. An example case is an online education service that usually gets five students in the period between 8 pm and 8:30 pm and needs to calculate the probability of getting seven students in that session.

A normal distribution is a very general type of distribution, which looks like a bell. Some of the examples are heights of men in India, measurement errors, IQs. In U distribution, points are more likely to be at the ends of a range than in the centre. For example, if 35% of students in a class get A+ grade, 35% get zero and the remaining 30% get grades in between, that would form a U distribution.

Weibull models are used to represent various types of perceived failures of components and events. They are mostly used in reliability and survival analysis. Weibull distribution can also provide a massive range of data from many other areas like economics, hydrology, biology, engineering sciences. It is an absolute value of probability distribution, which is frequently used to model the reliability, survival, wind speeds and other data.

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