Shatu Mshelia Written by Shatu Mshelia · 1 min read >

In a previous post – I shared a brief insight of what probability was. In this post, i plan to go a bit more indept into the basics of probability. You may consider this, “Pobability 101.”


Probability is a way to quantify the uknown (factors that are not evident), a way to account for what is not provided in experience. Knowing how to apply probability is instrumental when only some of the required data is provided. We must be able to draw inference using probability with the data available.

Probability is a way to put some order into what is largely, a chaotic world. This makes probability key in data analytics.


Probability is important in all works of life. We make the bulk of our basic daily decisions using probability. Hence, probability exists because we have to deal with uncertainty. We find that, a lot of the times, we make decisions in the midst of uncertainty. Probability helps us to plan with these uncertainty.

Moreso, in the business world we have to make plans every day about what is to come. For example, we plan budgets in business which we draw out fro estimates.

In the business environment, we are often face risks. Risk is related to uncertainty and we often find that one is mistaken for the other. However, they are not the same. They are the same in the sense tht both carry a certain level of the unknown (things you do not know) about them.

With uncertainty, we know not the probable outcomes of an event or liklihood of their occurence. Meanwhile, with risk, we know the possible outcomes of an event; and we have some information about each of the outcomes’ liklihood of occurrence. Basically, we define risk by an event’s possible outcome and probability.


There are three ways to assign probabilities;

  • Classical Method – We apply this when an equal probability can be applied to the outcomes in an event.
  • Relative Frequency Method – Involves using historical data.
  • Subjective Method – This is majorly intuitive and inferential.


In the decision making process, there are three major things that occur which you can tie to analytics.

  • Decision analysis with uncertainty – Just as the name implies, this involes making decitions with uncertainties.
  • Optimization – This sort of decision making does not involve uncertainties. Here, we make decisions with available facts and data, making sure to take the best or optimal cause of action.The one decision that pays the most or maximizes profit.
  • Sensitivity Analysis – This sort of decision making does not involve uncertainties. It takes into consideration, how one variable affects the other.

As managers, it is key that we are able to make the best decisions with the available information at every given point. As a result, in order to be able to make good decisions, it is important to understand probability. Else, we risk making decisions that are detrimental to the organizations we ought to enhance.


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