In statistics, data can be classified as either continuous or discrete. Continuous data can take on any value within a given range, while discrete data can only take on specific values within a given range.
Continuous Data:
Continuous data, such as time or distance, are often measured on a continuous scale. For example, the time it takes to run a race is a continuous variable because it can take on any value within a given range, such as between 10 seconds and 11 seconds. Continuous data are often modeled using continuous probability distributions, such as the normal or gamma distribution.
Continuous data can be visualized using bar charts, pie charts, etc.
Discrete Data:
Discrete data, on the other hand, can only take on specific values within a given range. For example, the number of children in a household is discrete because it can only take on specific values, such as 0, 1, 2, or 3. Discrete data are often modeled using discrete probability distributions, such as the binomial distribution or the Poisson distribution.
Discrete data is usually visualized using histograms, scatter plots etc.
In general, the choice between continuous and discrete data types depends on the nature of the data being collected and the type of analysis being performed. Continuous data are often used when "measuring" continuous variables, such as time or distance, while discrete data are often used when "counting" the number of occurrences of an event.
