Data Science · Chapter 18 of 43

Common Distributions

Distributions describe how values are spread. NORMAL (bell curve), UNIFORM (equal chance), BINOMIAL (successes in trials), POISSON (rare events).

Knowing the distribution guides which model or test to use.

Example 1 (python)

import numpy as np
x = np.random.normal(0, 1, size=1000)
print(x.mean(), x.std())

Output

~0, ~1

Sample from a standard normal.

Example 2 (python)

y = np.random.binomial(n=10, p=0.5, size=5)
print(y)

Output

[4 6 5 3 7]

Number of heads in 10 flips.

Key points

💡 Note: Central Limit Theorem: sums of many independent samples become approximately normal — a superpower for statistics.