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Monte Carlo sampling
See seeded sampling error and an approximate interval for a standard-normal mean.
What it shows
Monte Carlo sampling approximates a quantity by repeating random draws. For independent samples, the uncertainty of a sample mean typically decreases with the square root of the draw count.
Example
Increasing the number of independent draws from 100 to 400 should roughly halve the standard error of the estimated mean, although any single seeded run can differ.
Assumptions and limits
This demonstration draws from a standard normal distribution and uses a fixed random seed so the same inputs reproduce the same sample.
Its approximate interval reflects sampling uncertainty under that model. It is not a market forecast and does not cover model misspecification.
Educational calculations with stated assumptions. They do not predict live market performance.
Result
Run a calculation to see the result.