In the following 3 chapters, the author will talk about count data. It may sound easy to count. But, there are some subtleties to keep in mind when interpreting count data.

The author introduces a concept called Areas of Opportunity. Before we can define what is Areas of Opportunity and also give my own understand (I find my understanding is easier), we need to first talk about the difference between Counts of Items and Counts of Events. Areas of Opportunity is defined slightly differently.

The area of opportunity for counts of items is n. That is the largest possible count you can obtain. The area of opportunity for counts of events is the region within which the events occurred of space and time, of product, or others.

My own interpretation on the area of opportunity can be translated into a question: Count out of what? Relative to what? It can be all possible count, meaning the whole universe. It can be a particular time period. It can be a particular region. We need to ask ourselves, apart from how it is counted, count relative to what? It is like the denominator of the count.

In fact, if the denominator of the count varies, the count itself may be distorted. For example, the defect rate may be lower because you sampled fewer orders. The number of orders is the denominator/ area of opportunity of this count. Therefore, if the denominator varies, it is suggested we divided the count with the area of opportunity to get a rate, and also report the area of opportunity along with the count.

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