Statistics Interactive

Five core concepts, one page — explore them with sliders and animations.

1. Normal Distribution

The bell curve is the most important distribution in statistics. The μ (mean) controls the center, and σ (standard deviation) controls the spread. The 68–95–99.7 rule: ~68% of data falls within 1σ, ~95% within 2σ, ~99.7% within 3σ.

0.0
1.0
P(μ−σ to μ+σ)
68.27%
P(μ−2σ to μ+2σ)
95.45%
P(μ−3σ to μ+3σ)
99.73%
Normal PDF
±1σ
±2σ
±3σ

2. Central Limit Theorem

No matter what the original population looks like, the distribution of sample means approaches a normal distribution as sample size n grows. Drag the slider to increase n and watch the sampling distribution narrow and become bell-shaped.

1
Original (Uniform)
Distribution of Sample Means
Normal Fit
Mean of Sample Means
Std Error (σ/√n)

3. Correlation

The Pearson correlation coefficient r measures linear association between two variables, ranging from −1 (perfect negative) to +1 (perfect positive). Drag the points on the scatter plot to see how r changes in real time.

Pearson r
r² (Variance Explained)
n
20

4. Law of Large Numbers

As you repeat a random experiment, the average of results converges to the expected value. Click Roll to simulate rolling a fair die and watch the running average converge toward 3.5.

Rolls
0
Running Average
Expected Value
3.5

5. Bayes' Theorem

Bayes' theorem updates our belief about a hypothesis given new evidence. This is crucial in medical testing, spam filtering, and scientific inference.

P(A|B) = P(B|A) × P(A) / P(B)

Scenario: A disease affects p% of the population. A test has sensitivity (true positive rate) s% and specificity (true negative rate) t%. If you test positive, what's the probability you actually have the disease?

1.0%
99%
95%
P(Disease|Positive)
False Discovery Rate
P(Positive)
False Positives (α)
True Positives (1−β)