Turn probability into intuition. Explore distributions, simulate sampling, investigate relationships, and understand statistical decisions—all with charts you can inspect and export.
7 distributionsSeeded simulationsLocal data
Visual learning lab ● ● ●
YOUR INTERACTIVE WORKSPACE LOCAL
Explore a statistical question Ready
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THEORETICAL MODEL
Normal distribution
Selected range
Choose a distribution to explore its probability.
MONTE CARLO EXPERIMENT
Sampling distribution of the mean
⋮⋮⋮No simulation yet
Run a reproducible experiment to generate a histogram.
Your results will show how repeated sample means vary around the population mean.
ORDINARY LEAST SQUARES
Correlation & regression
Paste or load some paired observations to see the fitted model.
STATISTICAL INFERENCE
How surprising is the data?
Tail evidence
Run a hypothesis test to see the distribution, statistic, and decision.
Private, local-first analysis. Not a clinical or financial decision system.
A MORE INTUITIVE WAY TO LEARN
See the mathematics behind the decision.
Explore an assumption, rerun an experiment, or load your own data. Each lab is designed to connect a statistical formula with an interactive visualization.
01
Choose a model
Adjust distribution parameters or provide data with clear size and format limits.
02
Investigate the result
Inspect probabilities, histograms, fitted lines, residuals, and t-test evidence.
03
Export your work
Download a chart, reusable CSV, readable analysis, or your editable JSON project.
Educational tool. Probability calculations use numerical approximations; visualizations and p-values depend on the chosen statistical assumptions. Simulated random results are deterministic for a supplied seed and are not cryptographically secure. For regulated or consequential analyses, independently verify results with professional statistical software.
Using Statistics & Probability Lab
Choose a distribution, run a seeded sampling experiment or enter paired data for simple regression. Review supported t-test inputs and assumptions, inspect charts and keep editable JSON or export available datasets and reports in a handoff ZIP.
Probabilities are numerical approximations and may underflow in extreme tails. Regression intervals and t-tests depend on their stated sampling and error assumptions; the tool does not check them automatically or establish causation. There is no multiple regression, Bayesian inference or multiplicity correction. Browser drafts are best effort; save exported projects.
Choose a numerical experiment, data export or editable project
Distributions and sampling: explore supported numerical models with explicit parameters and seeds.
Regression/t-tests: review the supported calculation and its assumptions.
Project JSON: retain supported settings and data for continuing.
CSV, reports or ZIP: share available datasets and outputs with their assumptions and experiment context.
Practice: document a seeded sampling setup
Select a supported distribution and record its parameters and seed.
Run the sampling experiment and inspect the chart and available data.
Save the project and label exports with the setup, distinguishing simulated samples from observations.
This is an authored experiment plan, not a statistical finding or independently validated result.
Statistics workflow questions
Does the tool check assumptions or establish causation automatically?
No. Supported regression intervals and t-tests depend on stated sampling/error assumptions. The tool does not automatically validate those assumptions or establish causal conclusions.
Can I assume every tail probability or advanced model is supported?
No. Numerical tail underflow is possible, and multiple regression, Bayesian inference and multiplicity correction are outside the scope. Keep the editable project and inspect limits before interpreting results.
