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확률 — Probability & Statistics

Understanding probability
through visual intuition

Clean, honest explanations of probability concepts. No intimidation — just clarity.

-2σ -1σ μ +1σ +2σ Normal Distribution
P 0.5 0.5

From coins to distributions

Every complex probability concept starts with something simple. A coin flip. A die roll. A branching path. Learn the building blocks, and the rest follows naturally.

Probability Calculator

Enter values to see probabilities visualized in real-time.

Expected Value (np): 5.00
Std Deviation: 1.58
P(X = k) at mode: 0.2461

Binomial Distribution: P(X = k)

k (number of successes)

Core Probability Concepts

A B A∩B

Bayes' Theorem

Update your beliefs as new evidence arrives. P(A|B) tells you the probability of A, given that B has occurred. The foundation of modern inference.

P(A|B) = P(B|A) · P(A) / P(B)
n → ∞

Law of Large Numbers

As you repeat an experiment more and more, the sample average converges to the expected value. The chaos settles into predictable patterns.

n → μ as n → ∞

Central Limit Theorem

No matter what distribution you start with, the average of many samples will be normally distributed. The bell curve emerges from chaos.

√n (X̄n - μ) / σ → N(0,1)
x1 x2 E[X]

Expected Value

The weighted average of all possible outcomes. Think of it as the balance point of a probability distribution — where the fulcrum would go.

E[X] = Σ xi · P(xi)
Sample Space S Given B occurred

Conditional Probability

How does knowing one event change the probability of another? Conditional probability narrows the sample space and recalculates.

P(A|B) = P(A ∩ B) / P(B)
σ²

Variance & Std Deviation

How spread out are the values? Variance measures the average squared distance from the mean. Standard deviation is its square root — in the same units as the data.

Var(X) = E[(X - μ)²]