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PSYCHOLOGY & SOCIAL SCIENCES · STARTER PROJECT

What does the club survey show?

Explain a fictional club survey about meeting times without turning a small set of responses into a claim about everyone.

Beginner-friendly path · 8 guided lessons · editable JavaScript · runs in your browser · no API key

START WITH A QUESTION

Does a majority of respondents mean a majority of everyone?

Predict what changes when the denominator is respondents rather than all the people invited to answer.

Reveal what to look for

The reader exposes counts, missing responses, and the sampled group instead of turning a preference survey into a population or causal claim.

Start from the basicsExplore the path

8 guided lessons · learn at your pace

YOUR FINISHED BUILD

What does the club survey show?

A summary with counts, denominators, source IDs, and stated limitations.

WHY THIS FIELD USES THE IDEA

Social-science research requires clear sampling scope and denominators before interpreting what a survey appears to show.

SEE THE IDEAChoose a step

See the idea before you code it

Read the input

Anonymous synthetic response counts, invitation count, nonsensitive group labels, question wording, and collection method.

function solve(input, tools) { /* your workflow */ }

Click a step to inspect this project's flow. The final workspace runs your JavaScript with original, authored records and checks.

A COMPLETE PATH TO YOUR BUILD

One small step at a time.

You can begin at the first lesson, resume shared skills you already practiced, or open the project IDE when you are ready.

01 · UNDERSTAND

Start with the essentials

No coding experience needed. These common lessons stay completed across every project.

  1. 1A model is one part of the systemFirst understand a model, a fixed workflow, and an agent that chooses its next tool call.
  2. 2Your first few lines of codeStart from zero: read values, objects, functions, and the result your code returns.
  3. 3Make a rule you can testWrite a small condition, change the input, and observe how the output changes.
  4. 4Turn an idea into a clear goalTurn the project's deliverable into a specific goal, allowed actions, and a visible stopping rule.

02 · PRACTICE

Learn the tools your project needs

Short explanations, clickable diagrams, and a small coding lab for each skill.

  1. 5A tool needs a clear contractRead question wording and aggregate counts together.
  2. 6Return something another program can useReturn each count with its denominator and collection scope.
  3. 7Make a claim you can supportKeep an observation about respondents distinct from an explanation of behavior.

03 · BUILD

Make your project work

Follow the project-specific ideas, insert the explained snippets, and inspect your real execution trace.

  1. 8What does the club survey show?Build and test your own version: A summary with counts, denominators, source IDs, and stated limitations.

04 · TEST & REFLECT

Try to break it. Explain what holds.

3 runnable cases check the final project. Passing them demonstrates these examples, with limitations still to explore.

Change one thing

Keep the response counts fixed but increase the invitation count. Explain which share changes and which does not.

Keep a useful learning record

What could nonresponse change about the story, even when the arithmetic is correct?

Open project IDEReflect & save your notes
Inputs, checks, and project boundaries
Your starting material
Anonymous synthetic response counts, invitation count, nonsensitive group labels, question wording, and collection method.
What your agent must check
  • Use respondents for the specified share and retain two missing item answers.
  • Avoid division by zero and an invented preference.
  • Keep the evidence's actual population even when a broader scope is requested.
Keep the scope clear
No real student records, mental-health or personality inference, individual prediction, or causal conclusion from an observational preference survey.
Research behind this project path

Original explanations and authored practice records draw on these research and engineering ideas. The source organizations do not endorse this course or supply its fictional results.

NIH · Understanding Clinical Studies

Observational associations and randomized intervention designs support different kinds of conclusions.

This explains study design; it does not provide evidence for the fictional study cards in this lab. Direct page access was blocked during review; its primary indexed text was available.

Anthropic · Writing effective tools for agents — with agents

Design distinct tools with clear parameters, relevant returned information, and evaluations of how the agent actually uses them.

A description or schema does not guarantee the right action. A live tool can return different data for the same arguments as its environment changes.

Anthropic · Demystifying evals for AI agents

Define tasks, trials, and graders; inspect both execution records and final outcomes; repeat trials when model behavior varies.

A score depends on its cases and grading rules. Repeating a deterministic classroom case does not measure the variability of a live model.

Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.