Intuitive explanations for the Normal, Bernoulli, Binomial, Poisson, Exponential, Gamma and Weibull distribution — with Python example code
Statistical Distributions are an important tool in data science. A distribution helps us to understand a variable by giving us an idea of the values that the variable is most likely to obtain.
Besides, when knowing the distribution of a variable, we can do all sorts of probability calculations, to compute probabilities of certain situations occurring.
In this article, I share 7 Statistical Distributions with intuitive examples that often occur in real-life data.
The Normal or Gaussian distribution is arguably the most famous distribution, as it occurs in many natural situations.
A variable with a normal distribution has an average, which is also the most common value. Values closer to the average are more likely to occur, and the further a value is away from the average, the less likely it is to occur.
The normal distribution is also characterized by symmetric variation around the average, described by the standard deviation. This means that higher values are as common as lower values.
Examples of the normal distribution can be found in many variables that are natural, continuous variables. For example, the weight or height of animals would follow a normal distribution, as most animals are of the average weight, some are a little over or underweight but not so many are extremely skinny or extremely fat.
