![]() ![]() Continuous data can be as detailed as it is useful. Analytics tools can also get even more detailed. Physical measurements of continuous data, such as weight, is as detailed as the measurement tool allows. We often visualize this type of data in line graphs, trend lines, percentages, averages, and other non-stop, divisible methods.Ĭontinuous data allows for much more granularity and specificity. Continuous data is only limited by the practical volume of the data or the specificity of the measurements in question. In other words, continuous variables could go on forever. Continuous variables use increments that are dividable and subcountable. What is Continuous Data?Ĭontinuous data is data that is measurable, versus being countable. No one would count “17.5 devices.” Similarly, the number of employees at a business is a discrete value. A store’s inventory of computers is a discrete variable, because there are a set amount of computers within the inventory. ![]() For instance, the number of students in a classroom is a discrete value. Examples of Discrete Dataĭiscrete data often describes physical or material entities. It also allows users to work with discrete integers more quickly and easily. This can inhibit some level of specificity in discrete data values. Users can only subdivide discrete data to its smallest unit. It is a clearly defined set of values with determined boundaries, and thus fits this category.ĭiscrete data has limited granularity. A student cannot receive half of an A, and must receive a letter on the predetermined scale. It is important to remember that discrete data does NOT have to be numbers ! A perfect example of this is letter grading systems.
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