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.
Next: Continuous Distributions