Probability Distribution

Blessing Amaechi Written by Blessing Amaechi · 1 min read >

According to Wikipedia, Probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It tells what the probability of an event happening is. Also, it has to do with trying to forecast what the value of the designated random variable will be. In order to do that we need to understand the kind of generational process that produces that variable whether it is binomial or exponential etc.

Random variable is a numeric expression of the outcome of an event. A random variable can take any value in a continuum. Continuum means from 0 to infinite or finite 0 to 100. We have a lot of practical/real-life examples of random variables in the business organization. For example, the prediction of an organization’s profitability at the end of the year. They may make a profit or loss. Also, to determine how many employees might still stay in the company till the end of the year or for a certain number of years. Random variables might be discrete or continuous. Discrete random variables are those variables that you can count while continuous are those variables that you can measure. Both continuous and discrete are numeric or numbered. 

Probability is very important and significant in the world of business. Probability has to do with chance, likelihood, odd, and stochastic. All business decision has to do with chance and risk.  You take risks in business. In risk, you already know the probability of those outcomes but you don’t know what will happen. Like the stock market, it could be neutral, high, or low. You win, lose or it can remain the same. The three outcomes are defined, what is the probability? Those probabilities are what we are trying to assign probably we don’t know it, that is a risk. For example, by investing in a stock market you stand the risk of making or losing money. Probability distribution follows the same fundamental theory.

We have different types of Probability distribution. However, I will be dwelling on Binomial probability distribution for now. Binomial probability distribution has to do with the event of a variable that has two possible outcomes. That is success or failure. Only two outcomes are possible and they are mutual in the sense that one precludes the occurrence of the other. They are contradictory, success or failure. We normally come across binomial probability in our day-to-day daily activities and decision-making. As a student that is pushing an academic learning qualification, there is a probability that you can succeed by acquiring the necessary knowledge and thereafter graduating with your mate or failing to get the required learning and knowledge and not graduating with your mate and also getting the qualification. Before you can bring in a binomial distribution, you will first cast the event as the binomial one, where there are two outcomes, and if those outcomes are mutually exclusive and collectively exhaustiveness then you have a binomial experiment.

We will continue with the other types of probability distribution next time.

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