Q 81. In what circumstances can discrete data be treated as continuous data? 

 

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Working with sample means

When we work with sample means, the data from any distribution, even discrete are subjected to the properties of normal distribution, as governed by the central limit theorem. The application of this concept enables the usage of normal distribution laws for tools such as control charts.

Ordinal data

Many a time when we use ordinal data on a likert scale with ratings 1 to 5. When we average such recordings for a particular parameter from various respondents, it will get converted into a metric that can be seen on a continuous scale.

Histogram

Every time we plot a histogram even for data of discrete nature, (for example no. of corrections in a document per day), with large amount of data, it tends to exhibit the behavior of continuous data, say normal distribution.

FMEA ratings

When we use the ratings in FMEA for severity, occurrence and detection, we assign discrete rankings between 1 and 10, but once converted to RPN, it becomes more continual in nature, though it may remain a whole number.

Failure data / distributions

Another situation I can think of is about failure data. Individual failure data are count of occurrences, obviously discrete to start with. However, when we convert it to failure rate and plot distributions against time, they are treated as continuous distributions such as exponential, Weibull etc.

When we have huge set of discrete data for process and most data are non-zero[or tense to zero] we can use it as continuous data in statistical tool. Check for bell curve and then got for what ever you needed.