Did you know that you can navigate the posts by swiping left and right?
The following is the definition of probability distribution taken straight from wikipedia.
In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. Examples are found in experiments whose sample space is non-numerical, where the distribution would be a categorical distribution; experiments whose sample space is encoded by discrete random variables, where the distribution can be specified by a probability mass function; and experiments with sample spaces encoded by continuous random variables, where the distribution can be specified by a probability density function. More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more general probability measures.
The following is a rule of thumb to be applied for modelling distributions in continous and discrete case.
We apply Bernoulli in the case where we want to model one occurrence of a success or failure trail. For example a coin-flip experiment can be thought of a Bernoulli Experiment where there are only 2 outcomes, heads (aka success) or tails(aka failure).
We apply Binomial where we model a number of success runs out of the total number of n runs, each with a probability of success p. Say suppose components are packed in boxes of 100. The probability of a component being defective is 0.2. Say if we want to find the probability of 5 components being defective we use Binomial Distribution for it.
With Poission, we model the number of events that occur in a fixed interval. These events occur at some average rate independently of the previous events. We model traffic events using Poisson
Geometric is nothing but sequence of Bernoulli trials until first success (p)
Uniform is used in case where any of the values in the interval of a to b are equally likely
Commonly occurring distribution shaped like a bell curve. This often comes up because of the Central Limit Theorem (to be discussed later)
We model time between Poisson events, where these events occur continously and independently.
Adios!