Interactive Statistical Learning

Statistics
Visualizer

See the math. Drag the data. Understand intuitively. Every concept visualized, every formula made tangible.

by Phan G. Anh-Thu

25
Core Modules
8
Supplementary
5
Parts
How This Course Works

Learn by Doing

Drag sliders, add data points, watch distributions change. Every concept is interactive — you don't just read, you explore.

x̄ = Σx / n
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Visual Insight

Formula to Intuition

See what formulas actually do. Watch means shift with outliers, see variance spread, understand p-values visually.

Music Medical Business Climate

Real-World Context

Switch between domains — see the same statistics applied to music, medicine, business, and climate data.

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Step-by-Step

Each concept builds on the last. Master fundamentals before advancing to inference, regression, and beyond.

All Modules

23 modules across 5 parts
01

Fundamentals

The building blocks of statistical thinking

MODULE 00
Chart Types
Which visualization for which data? Bar, line, scatter, histogram — choose wisely.
Bar Line Scatter Histogram
MODULE 01
Types of Data
Nominal, ordinal, interval, ratio — understand the four fundamental data types.
Nominal Ordinal Interval Ratio
MODULE 02
Central Tendency
Mean, median, mode — see when and why each measure matters.
Mean Median Mode Outliers
MODULE 03
Measuring Spread
Variance, standard deviation, and the shape of distributions.
Range IQR Variance Std Dev
MODULE 04
Probability Distributions
Normal, binomial, Poisson — sculpt curves and see patterns.
Normal Binomial Poisson Uniform
MODULE 05
Correlation & Causation
Relationships between variables and the causation trap.
Scatter Plots r Coefficient Causation
02

Inference & Testing

Making decisions from data

MODULE 06
Hypothesis Testing
p-values, significance, Type I & II errors visualized.
Null Hypothesis p-value Errors Power
MODULE 07
t-Tests
Compare groups and see statistical significance emerge.
One Sample Two Sample Paired Effect Size
MODULE 08
ANOVA
Compare multiple groups and decompose variance.
F-statistic Between Groups Within Groups
MODULE 09
Chi-Square Tests
Categorical data relationships and independence.
Contingency Expected Frequencies Independence
03

Regression & Prediction

Modeling relationships in data

MODULE 10
Simple Linear Regression
Add data, watch the line fit, understand least squares.
Slope Intercept R² Residuals
MODULE 11
Multiple Regression
Many predictors, 3D visualization, variable importance.
3D Plane Partial Effects Multicollinearity
MODULE 12
Model Comparison
Same data, different models side-by-side.
Linear Polynomial Exponential
MODULE 13
Logistic Regression
Binary outcomes, S-curves, classification boundaries.
Probability Odds Ratio ROC Curve
04

Advanced Concepts

Deeper statistical methods

MODULE 14
Confidence Intervals
Uncertainty visualization and what CI really means.
Bootstrap 95% CI Interpretation
MODULE 15
Bayes' Theorem
Prior, likelihood, posterior — see beliefs update.
Prior Likelihood Posterior
MODULE 16
Time Series Analysis
Trends, seasonality, forecasting with uncertainty.
Decomposition Forecasting ARIMA
MODULE 17
Clustering
k-means, hierarchical — watch groups emerge.
k-means Hierarchical Dendrograms
MODULE 18
PCA
Dimensionality reduction visualized in 2D/3D.
Components Variance Explained Scree Plot
MODULE 19
Effect Sizes & Power
Measuring practical significance and designing adequate studies.
Cohen's d Power Analysis Sample Size
05

Research Methods

Designing and reporting statistical studies

MODULE 20
Non-parametric Methods
Distribution-free tests for when assumptions fail.
Mann-Whitney Wilcoxon Kruskal-Wallis
MODULE 21
Missing Data
Understanding patterns and handling incomplete data.
MCAR/MAR/MNAR Imputation MICE
MODULE 22
Experimental Design
Planning studies that establish causal relationships.
RCTs Randomization Controls Factorial
MODULE 23
Statistical Reporting
Communicating results with clarity and transparency.
APA Format Effect Sizes Pre-registration
Deep Dives

Supplementary Modules

Advanced topics and foundational concepts for deeper understanding

S01
Sampling Methods
Simple random, stratified, cluster, and systematic sampling techniques.
Random Stratified Cluster
S02
Central Limit Theorem
Why sample means approach normality regardless of population shape.
CLT n >= 30 Sampling Dist
S03
Z-Scores
Standardizing values for comparison across different scales.
Standardization 68-95-99.7 Percentiles
S04
Standard Error vs SD
Understanding the crucial difference between these measures.
SD SE Error Bars
S05
Two-Way ANOVA
Analyzing main effects and interactions between two factors.
Factorial Interaction Main Effects
S06
Repeated Measures ANOVA
Within-subjects designs and handling correlated observations.
Within-Subjects Sphericity Corrections
S07
Factor Analysis
Discovering latent variables underlying observed measurements.
EFA CFA Loadings
S08
Survival Analysis
Time-to-event data with censoring and hazard functions.
Kaplan-Meier Cox Censoring

References & Sources

Verify formulas and explore deeper with these trusted resources:

Khan Academy: Statistics StatQuest OpenIntro Statistics Wikipedia: Statistics NIST Statistics Handbook