In the world of data analysis and statistics, continuous and discrete data play fundamental roles. These two types of quantitative data serve different purposes as people use them to draw valuable ...

Discrete data includes discrete variables that are finite, numeric, countable, and non-negative integers. In many cases, discrete data can be prefixed with “the number of”. For example: ...

A dripping tap shows discrete data, because each individual drop can be counted. The data collected will simply show the number of drops. A gushing tap is an example of continuous data.

For example this could be the average cumulative return over five years. ... Discrete data meanwhile shows the return in each individual period separately, ...

For example, you cannot have half a dog or half a cat. European shoe sizes are another example of discrete data. You can be a size 36, or a size 38, but you cannot be a size 36.1 or a 38.3.

Discrete data consists of whole numbers that are counted rather than measured. For example, when you track items sold, the data is considered discrete -- you don't normally sell half an item.

Examples of Unstructured Data. Think about any kind of data that doesn’t have a recognizable structure and you have identified an example of unstructured data.

Discrete data is easy to identify, manage, and analyze. ... For example, you are trying to improve your production line ...

Discrete data meanwhile shows the return in each individual period separately, typically each individual year’s performance although discrete months and quarters are also common.

Distinguishing between discrete vs. continuous data and situations that call for each data type is important in ensuring you get your desired results.