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I was going thru a course on probability and probability distributions for data science and in that they were describing the various probability distributions with mathematical formulae. I was wondering how does a data scientist or a statistician use probability distribution in real life? Could someone please provide couple of real life examples on how these are used? Wanted to understand the application of these as just reading theory did not give me the intuition on how to use them.

Many thanks.

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Probability and statistics form the basis for a lot of the classical Machine Learning algorithms. Apart from that, a Data Scientist or a Statistician uses probability directly in the following areas:

  • Hypothesis Testing
  • Statistical Modelling
  • Inference

Not an exhaustive list


To give a real life example, consider you want to model a phenomenon with a binary outcome. You have a certain underlying probability of the occurrence of the event which you want to find, say p.

A data scientist may choose to model the phenomenon using a Bernoulli distribution. After employing a parameter estimation technique like Maximum Likelihood Estimation, the data scientist would find that an estimate of $p$ is $\frac{\Sigma_{i=1}^n x_i}{n}$. Maximum likelihood Estimation requires you to understand the basic probability distribution for the kind of model that you assume your data to be coming from. One can similarly model other processes using various other distribution models.

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