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A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

This definition is still not clear to me. I'm looking at a data table showing the expected frequencies of a data distributions. To me this looks discrete, but by definition it should be continuous. This data is measurable, it's not data you can count. The probability falls on an interval from [0,1], to me that is a infinite possible indicating it is a continuous. Can someone help me confirm?
2 0.02464
3 0.05113
4 0.09223
5 0.12772
6 0.159
7 0.159
8 0.13038
9 0.11141
10 0.07198
11 0.04517
13 0.02735
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    $\begingroup$ It's not the probabilities that you should be concerned with, but rather the values $2,3,\ldots,13$. $\endgroup$
    – angryavian
    Aug 22, 2021 at 1:29
  • $\begingroup$ Can you elaborate? $\endgroup$
    – Johnny Apple
    Aug 22, 2021 at 1:38
  • $\begingroup$ There are only 11 (finite) possible outcomes $\endgroup$
    – Rezha Adrian Tanuharja
    Aug 22, 2021 at 1:41

1 Answer 1

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A continuous probability distribution would have 0 as the expected frequency of any value in its range, whereas a discrete distribution takes positive expected frequency on a specific set of values.

What you’re showing on your table of frequencies is a discrete distribution over the integers 2-13

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