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Stochastic Models
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1.1Symmetric Random Walk1.2Rescaled Random Walk1.3Standard deviation1.4Central Limit Theorem2.1Defining the Wiener process2.2Increment explorer2.3Joint distributions and covariance3.1Running maximum and minimum3.2Reflection principle3.3First passage times3.4Hitting probabilities
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Curriculum/Chapter 1 · Discrete to Continuous/1.4
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1.4

Central Limit Theorem

Why does adding up many small, independent shocks always produce a bell curve — whatever the shocks look like?

Goal: watch the standardised sum Zₙ = Sₙ/√n approach N(0,1) for different step laws, and measure how fast the gap closes.

Simulation controls

Step distributionAll three have mean 0 and variance 1.
Summed steps nZₙ = Sₙ / √n
5,000
100020000
7
19999
Predict first

Standardize the sum of 30 exponential steps (single-step skewness 2). How skewed is the result?

Distance from normal

Empirical variance
1.010
Theoretical variance
1.000
Empirical skewness
1.902
Theoretical skewness γ/√n
2.000
KS distance
0.1587
Berry–Esseen bound
1.1464

Start at n = 1 with the skewed law: the histogram is the step distribution itself. As n grows, the skewness shrinks like 1/√n and the histogram settles onto the normal curve — for every step law.

Distribution of Zₙ = Sₙ/√n vs N(0,1)
n = 1
Empirical densityN(0,1) density

The coin's sum lives on a lattice, so small n shows spikes; the continuous laws smooth out sooner. Only mean and variance survive the limit.

Distance to N(0,1) vs n
Berry–Esseen boundMeasured KS distance

Log–log axes: a 1/√n rate is a straight line of slope −½, like the dashed Berry–Esseen bound. The dotted line (0.012) is the sampling noise floor — below it the finite sample, not the sum, sets the distance.