Teach with StatsCapybara

Ninety-seven interactive lessons and a shelf of tools, free and open to anyone. If you teach statistics or research methods, everything here is yours to use in a course: link it, embed it, print it, and assign from it. This page shows how.

What your students see

No accounts. No login wall. No email capture. A student opens a lesson and starts reading and playing with it in the same second. That is the whole experience, and it is deliberate.

Progress lives only in the student's own browser. Finished lessons fade back in the lists, courses grow a completion ring, and the homepage offers to resume where they left off, all from localStorage on their machine. None of it is sent anywhere, so there is nothing for you to administer and nothing for them to worry about. Clearing browser data resets it; using a different device starts fresh. It is a study aid, not a gradebook.

The site does count page views anonymously with Google Analytics, configured to set no cookie, and notes anonymously when a feature such as search, the quiz or an embed is used. That is how I learn which lessons get read and which tools are worth maintaining. No student is identified and no coursework data exists to collect, so assigning StatsCapybara needs no data-processing paperwork. The privacy page spells all of it out.

The site works offline once visited, prints cleanly, and reads well on a phone, so it fits however your students actually study.

Link lessons from your syllabus or LMS

Every lesson has a clean, permanent URL, so you can point at exactly the right section for a given week:

• A lesson: statscapybara.com/stats-1/central-limit-theorem/
• A calculator: statscapybara.com/power.html for the sample-size question that comes up in every methods class.
• The test chooser: statscapybara.com/which-test.html when students ask "which test do I run?"

Paste those into Canvas, Moodle, Blackboard, a slide, or a printed handout. They resolve the same everywhere because the whole site uses relative paths and plain HTML.

Printable handouts and posters

Every lesson prints as a clean handout: the prose, the frozen visualization, the worked APA example, and the code, with all the site chrome stripped away. Students hit Ctrl/⌘ P on any lesson and get study notes. The formula sheet prints the same way.

Three one-page posters are built to pin up or hand out, and each fits a single sheet at A4 and US Letter:

• Which test should I use? — the decision map as four grids.
• APA reporting — how to write up t, F, χ², r, and regression, with the italics and p-value rules.
• Assumption checks — what to check per test, how, and the fix when it breaks.

Assignments on reproducible datasets

The practice datasets are real CSVs with a short backstory, a suggested exercise, and a worked solution. They are seeded, so every student who downloads a file gets the identical numbers. A whole class can run the same analysis and land on the same answer, which makes marking straightforward and makes "my results are different" a real conversation rather than a mystery.

Point students at a dataset for homework, then have them check their output against the lesson's SPSS/JASP walkthrough and APA example. The numbers were all verified against professional software.

A ready-made semester project

If your course ends in a mini-project rather than an exam, One Study, Start to Finish is the model answer you can hand out or withhold. It carries memory-2x2.csv through every stage a project report has to cover: the research question and its operationalization, the design and a power analysis, the data checks, the assumption checks, a two-way ANOVA with simple effects, effect sizes with confidence intervals, the APA results paragraph, and the limitations. Every number in it is reproducible from the CSV.

Two things in it are hard to teach from a clean example and are deliberately left messy. The study recruited 25 per cell against a plan that asked for 32, so the guide states the shortfall and puts it in the limitations rather than hiding it. And a boxplot rule flags four points in one cell that should not be deleted, where deleting them would make the hypothesis look stronger. Both make good seminar discussions on their own, with or without the rest of the guide. Assign a different dataset from the same page and students cannot copy the numbers, only the structure.

Problem sheets you can hand out

The practice problems page is the pencil-and-calculator half of the same idea: exam-style scenarios with numbers small enough to work by hand, each followed by a full solution that shows the formula, the substitution, the arithmetic and the reporting sentence. Every value in every step was verified before it shipped.

Two print buttons at the top of that page make it a ready-made handout. Print problems suppresses every solution, whatever a student has left open on screen, so you get a clean problem sheet. Print with solutions forces all of them open for a marking key. Problems are tagged warm-up, exam-level or stretch, so you can pull a seminar's worth from one course without reading all forty-three. The last set is different in kind: six studies, figures and results sentences with something wrong in them, and no arithmetic to do. They work well as a seminar discussion or a take-home critique exercise.

Revision: quiz, mock exams and flashcards

For review weeks, the quiz runs two ways. Practice mode gives instant feedback one question at a time. Exam mode sets a mock paper over whichever courses you choose, at 10, 20 or 40 questions, then marks it at the end and hands back every miss with an explanation and a link to the course to revisit. There is an optional timer, and it counts up rather than down. The glossary flashcards run a spaced-repetition drill over every term the course defines. All of it is self-paced and, like everything else, keeps its state in the student's browser.

The exam results screen prints cleanly, so a student can hand you a marked mock paper, or keep it as a revision list.

Embed a live visualization

Any lesson can render as a bare interactive widget by adding ?embed=1 to its URL. That hides the navigation, sidebar, prose, and footer, leaving the eyebrow, the title, and the interactive itself with its controls. It is meant for an <iframe> in your slides, notes, or LMS page.

Copy this and change the lesson path to whatever you want to show:

<iframe
  src="https://statscapybara.com/stats-1/central-limit-theorem/?embed=1"
  width="100%" height="640" loading="lazy"
  style="border:1px solid #e2e8f0;border-radius:14px"
  title="Central Limit Theorem — StatsCapybara"></iframe>

The widget carries a small footer linking back to the full lesson, which opens in the top window rather than inside your frame. The lesson's canonical URL still points at the full page, so an embed never competes with the real lesson in search.

Embed a specific setting, not just a lesson

A plain embed opens at the lesson's default configuration, which is rarely the one your slide is about. Several lessons accept extra parameters in the URL, so the widget arrives already set the way you want it: the central limit theorem at n = 50, a correlation of exactly .30, a signal-detection observer with a conservative criterion. Add them alongside ?embed=1, in any order.

<iframe
  src="https://statscapybara.com/stats-1/central-limit-theorem/?embed=1&n=50&pop=skewed"
  width="100%" height="640" loading="lazy"
  style="border:1px solid #e2e8f0;border-radius:14px"
  title="Central Limit Theorem at n = 50 — StatsCapybara"></iframe>

The same parameters work on the full lesson page too, so you can put a configured link straight in your slides or notes without embedding anything. Anything the lesson does not recognize is ignored, and a typo just loads the ordinary default; nothing breaks in front of a class.

Values outside a listed range are pulled back to the nearest one the control can actually reach, and a value between two slider steps snaps to the nearer step. What you get is always a state a student could have reached by hand.

One thing to expect: a preset fixes the setting, not the sample. On lessons that simulate data, every visit draws a fresh random sample, so a scatter set to r = .30 will look a little different each time you open the slide and the observed coefficient will wobble around the target. That is the sampling variability the lesson is teaching, and it is worth pointing at rather than hiding.

A one-semester map

Here is one way to lay Stats 1 and Stats 2 over a typical fourteen-week intro course. It is a starting point, not a prescription: merge weeks, skip the ANOVA that your syllabus saves for a second course, or spend two weeks where your students always struggle.

Teaching research methods too? The Research Toolkit track adds full courses on Methods, Data, Writing, and Ethics, which map neatly onto a dissertation or capstone module. For a quantitative-methods sequel, ML & AI closes the Statistics Core and picks up where regression leaves off.

Use it freely

StatsCapybara is free and stays free. There is no institutional license to sign, no seat count, and no ambiguity to resolve: link it, embed it, print it, and assign from it in any class, at any institution, at no cost.

The one ask is a small one. If it helps your students, a visible link back (from your syllabus, your course page, or your notes) helps other teachers find it, and helps keep the lights on. If you build something with it, spot an error, or wish a certain tool existed, say hi. I read every message.