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A discrete variable might take on values in sets like these:
- 1, 2, 3, 4, 5, 6, ... (an infinitely large set)
- ..., -1001, -1000, ..., -3, -2, -1, 0, 1, 2, 3, ... (an infinitely large set)
- 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135 (a finite set)
'Discrete' here really means 'separate'.
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What is the difference between discrete data and continuous data?
Discrete variables have numbers that can be counted. Continuous data is measurable. Discrete data are data which can only take on a finite or countable number of values within a given range. Continuous data are data which can take on any value. It is measured rather than counted. The mass of a given sample of iron is continuous; the number of marbles in a bag is discrete.
Is data continuous if it can take on any value?
Yes.
What is the difference between continuous sampling and discrete sampling?
Continuous data is data that can theoretically be any amount at all. There may be a maximum and minimum value, but all numbers in between are possible. For instance, the amount of time it takes to complete a task, might be 13.4864 seconds. This makes the time continuous data. Discrete is data that can only be set amounts. For instance, how much money someone has, or how many times they've been to the cinema. You cannot have 13.4864 cents, or have visited the cinema 7.33 times. This is a discrete value.
Is age continuous data?
Age is typically considered to be continuous data. It can be measured as a precise number, such as 25.5 years, and can take on any value within a given range. However, in certain contexts, age may be treated as discrete data if it is categorized into distinct groups or intervals (e.g., 0-10, 11-20, 21-30 years).
What are the differences between discrete and continuous distribution?
discrete distribution is the distribution that can use the value of a whole number only while continuous distribution is the distribution that can assume any value between two numbers.
