Tools for random sampling: a random selection among a large number of elements of a subset (number or percentage) where it is chance that draws/decides.
Random Sampling - dCode
Tag(s) : Algorithm
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Statistical sampling of a set of elements (called population) is a method of selecting a subset of the population (called a sample).
Generally random, dCode sampling is based on pseudo-randomness generation algorithms.
Sampling a population allows to estimate its characteristics (according to a confidence interval) by measuring only a part of the population (only the sample) and extrapolating these measures (which is often faster and less expensive).
Unbiased sample creation (without criterion) is equivalent to a random selection/draw/choice without replacement.
Enter the entire population and the number of elements to be drawn (or a percentage of the number of elements to be drawn, the number of elements to be selected will be automatically calculated).
The program will generate a selection of a random subset from the requested list.
Example: In the list of numbers 0,1,2,3,4,5,6,7,8,9, a sample of 10% will return 2 values randomly
Write the element list as one element per line, or all the elements at once, but they must not contain spaces.
Sampling must balance the number of samples (and therefore the time needed) and the precision of the result (which increases as the sample grows), for this it is customary to decide, a priori, a confidence interval, generally 95% for scientific studies. This means that the results of the poll / sample will be reliable 95 times out of 100.
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Cite as source (bibliography):
Random Sampling on dCode.fr [online website], retrieved on 2022-11-29,