Generate random integers between a minimum and maximum. Prepare sample values for games, tests, or probability exercises.
How to use
Set whole number bounds and the number of results. Generate the list. The minimum and maximum are both possible outcomes.
Example
With minimum 1 and maximum 10, every result is an integer from 1 through 10, including both endpoints.
FAQ
Can I get a list without repeats?
This generator samples independently and allows repeats. Use a list picker with your own removal process when duplicates are unsuitable.
Related tools
A practical task
Map the range to the task explicitly. If a list has 12 entries numbered 1 through 12, use those inclusive bounds. A zero based array uses a different position scheme, so translate the number carefully in code.
Another useful question
Are the results sorted or unique?
The generator returns independent draws. Sorting or removing duplicates afterward changes how a list is used, so do that only when your task calls for it.
Settings and formats
| Field | Values |
|---|---|
| Minimum | Starting value: 1 |
| Maximum | Starting value: 100 |
| Count | Starting value: 10; bounds: 1 / 1000 |
Details for interpreting the result
| Detail | Explanation |
|---|---|
| Independent samples | Independent draws do not remember previous outcomes. A repeat does not indicate a failure, and a run of one outcome does not force the opposite outcome next. Evaluate the sampling rule rather than the appearance of a short list. |
| Equal chances | Uniform sampling gives each eligible entry the same chance in a single draw. Equal chance does not mean equal counts in a short run. The definition of an entry matters when a list contains duplicates. |
| Single draw probability | With n equally likely eligible outcomes, the probability of one named outcome is 1 divided by n. A result observed once does not tell you the probabilities; those come from the defined sampling process. |
| Short and long series | Random variation is especially visible in short series. A sample can look unbalanced without contradicting the model. Choose the experiment length before reviewing outcomes so the stopping rule does not depend on an attractive result. |
| Recorded experiments | For an exercise, write down the rule, eligible outcomes, and observations separately. That lets another person distinguish what the generator was allowed to produce from what happened in a particular run. |
| Mapping a result to a choice | Assign meanings to possible outcomes before generating. Changing the mapping afterward introduces personal selection into the decision. Keep the same interpretation for a recorded trial unless the rules explicitly call for a new experiment. |