Discrete Distributions

In the previous chapter, we learnt about the Continuous distributions. As a continuation, we are going to cover the other type of probability distributions which is “Discrete Distribution” 

Remember that, just like the continuous distributions, the discrete distributions too are part of the Data Gathering and Representation Techniques sub-group of the tools and techniques in quantitative risk analysis. 

Another point to note here is the fact that, the PMBOK Guide gives only a very high level or brief description of discrete distributions. We too will be covering only the basic facts (probably in a bit more detail when compared to the PMBOK but not in great detail) that are essential for you to know and understand from the PMI RMP Examination perspective. 

Discrete Distributions are 

• Used to show uncertain events where the probability of occurrence can be calculated accurately
• Based on a whole number 

Discrete Distributions are used to represent: 
• Possible scenarios in a decision tree
• Results of a prototype or
• Results of a test 

The following is a sample discrete distribution: 



As you can see, it contains various bars, each of which represents a possible outcome/option. Each outcome is assigned a probability and the sum of all of the outcomes must add up to 1 or 100% 

For ex: If you flip a coin, there is a 50% chance that you will get a head and another 50% for the tail. So, if we sum up the probability of both these outcomes you get 100%. If you create a Discrete Distribution for this case, you will see two equal sized bars each towering up to 0.5 respectively. 

There are several types of discrete distributions, like:
a. Discrete Uniform
b. Binomial
c. Hypergeometric
d. Etc… 

The point here is that, these are not part of the exam syllabus and hence are out of scope of our current discussion. From the PMI RMP Exam perspective, all you need to remember about the discrete distributions is: 

• They show uncertain events such as outcome of decisions or tests
• They represent several possible outcomes
• Each outcome is assigned a probability and each bar in the image represents an outcome
• The sum of all these probabilities works out to 1 or 100% 
• They are used in decision tree analysis. 


Prev: Continuous Distributions

Next: Sensitivity Analysis

Introduction to Probability Distributions

In the previous chapter, we looked at one of the tool and technique used in quantitative risk analysis which was called “Interviewing”. In this chapter, we are going to start with another tool that is used in this quantitative analysis process called “Probability Distributions”.

As with Interviewing, the probability distributions too are part of the data gathering and representation techniques sub-group.

We all know what Probability is, isn’t it?

In general, probability refers to the likelihood that a risk or any event for that matter will occur. It is usually represented numerically as a number value between 0 and 1. The closer the value is to 1, the greater the probability of the event occurring. Similarly, the closer the value is to 0, the lower the chances/probability of that event happening.

A Probability Distribution graphically displays data and represents both the probability as well as time/cost elements. So, by seeing a distribution, we can not only get an understanding of the probability but also the impact it will have on other elements like time or cost.

This chapter is just the introduction, if you aren’t too clear on what these distributions are, don’t worry. As we start looking into each of these distributions in detail, you will get a good idea of what these are and how they are used in the quantitative risk analysis process.

In the subsequent chapters, we will be covering two types of distributions, namely:

1. Continuous Distributions and
2. Discrete Distributions

Let us wrap up this chapter with a disclaimer that, there are numerous types of probability distributions. We won’t be covering all of them as part of this series. Also, the whole topic of probability distributions is very complicated. We will only cover those distributions that are part of the RMP Exam syllabus, as well as, whatever is required to be known from the RMP Examination perspective.

We will be covering a lot more than what the PMBOK tell us about these distributions but this isn’t an exhaustive reference material in this topic.

Let us start with Continuous Distributions which is the topic of the next chapter.

Prev: Interviewing

Next: Continuous Distributions