Number - Random (Stochastic|Independent) or (Balanced)

> (Data|State) Management and Processing > (Data Type|Data Structure) > Number, Numeric, Quantity

1 - About

Think of randomness as a lack of pattern.

Something random should be unpredictable. We shouldn’t be able to predict the next value of the sequence

The degree to which a system has no pattern is known as entropy.

See also:

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3 - Example of bad random sequence

Type Sequence Example
Uniform sequence 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Repeated sequence 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
Too many low numbers 1 3 2 5 3 9 1 2 4 2 5 1 1 2 8 1 5 2 3 4
Too many even numbers 2 8 4 6 0 9 8 2 4 8 6 4 2 2 5 1 4 8 6 2

4 - Type

4.1 - True

A high-entropy source is completely chaotic, is unpredictable, and is called true randomness.

Where computers fail at creating true randomness (see pseudo), the physical world does not. Truly random seed numbers can be derived from the unpredictability of physical processes itself (radioactive decay, imprecision of taking measurements)

A truly random distribution is:

Demo with pseudo number:

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4.2 - Pseudo

As digital computers are deterministic, truly randomness within a computer doesn't exist.

It turns out to be mathematically impossible to generate true random numbers using a digital computer, but it is possible to generate pseudo-random numbers. See Number - Pseudo-random Numbers

Pseudo-random distribution.

5 - Random Subject

6 - Test

6.1 - Graph

7 - Documentation / Reference

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