What is a random number and why should I care
summary
Claims that no such thing as a random number and that practical randomness comes from PRNGs are demonstrated with concrete tools: a linear congruential generator, seeding via random_device and uniform_int_distribution from the C++ <random> header. Pitfalls include biased modulus sampling, thread/seed issues and warmup/seed‑sequence subtleties. A visual demo races blobs out of a paper bag using different distributions to show behavior differences; the demo runs and makes the consequences tangible.
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chapters (13)
- 0:00Random numbers
- 5:24Why random numbers matter
- 9:17Pseudo-random numbers
- 15:45Linear congruential generators
- 19:45Mersenne Twister
- 26:11Randomness vs chaos
- 32:11Dice rolls and distributions
- 37:17Seeding generators
- 43:21Shuffling and sampling
- 49:25Testing with random inputs
- 53:46Distributions and outcomes
- 58:08Defining randomness
- 1:00:57Q&A
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