Report uncertainty instead of fake precision

Before you begin

• Basic statistics; inspect the worked fixture before beginning.
• A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt.
• For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory.

Free original, non-accredited practice. Self-review is not independent assessment. No particular provider, payment platform or country is mandatory; use a legitimate available route or explicitly labelled manual practice.

Describe the observed sample

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. Distinguish a count and a mean from a future prediction. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

SOURCE (authored fixture):
{
  "values": [
    2,
    4,
    4,
    6,
    9
  ]
}
INDEPENDENT FIXTURE TARGET:
{
  "count": 5,
  "mean": 5,
  "minimum": 2,
  "maximum": 9,
  "range": 7
}

Your practical task

1. Distinguish a count and a mean from a future prediction.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record a manual prediction and the relevant rule before consulting the answer key.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Describe the observed sample — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Replace 9 with 90: mean becomes 21.2, exposing sensitivity to an outlier.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Calculate spread by hand

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. Use minimum, maximum and range to reveal what a mean hides. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.

Worked example

MANUAL BASELINE / compare every named field
{
  "count": 5,
  "mean": 5,
  "minimum": 2,
  "maximum": 9,
  "range": 7
}
Keep a separate observed-result column; the answer key is not a model response.

Your practical task

1. Use minimum, maximum and range to reveal what a mean hides.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Calculate spread by hand — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Empty sample: reject an undefined mean.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Explore a reproducible simulation

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. If simulating, record seed and assumptions rather than calling synthetic draws real observations. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.

Worked example

PROJECT CONTRACT:
Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.
HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together.
EXPECTED CONSTRUCTED RESULT:
{
  "count": 5,
  "mean": 5,
  "minimum": 2,
  "maximum": 9,
  "range": 7
}

Your practical task

1. If simulating, record seed and assumptions rather than calling synthetic draws real observations.
2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Repeating the same five observations does not create independent new evidence.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Explore a reproducible simulation — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Repeating the same five observations does not create independent new evidence.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Stress-test an outlier

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. Compare the before/after mean without inventing a cause. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.

Worked example

CHANGE TESTS — predict before running:
1. Replace 9 with 90: mean becomes 21.2, exposing sensitivity to an outlier.
2. Empty sample: reject an undefined mean.
3. Repeating the same five observations does not create independent new evidence.
Keep the original expected.json unchanged.

Your practical task

1. Compare the before/after mean without inventing a cause.
2. Open failure-tests.md from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Replace 9 with 90: mean becomes 21.2, exposing sensitivity to an outlier.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Stress-test an outlier — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Replace 9 with 90: mean becomes 21.2, exposing sensitivity to an outlier.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Update a claim when evidence changes

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. Explain which assumptions would be needed for inference beyond this sample. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

REVIEW BOUNDARY:
Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.

ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit.

Your practical task

1. Explain which assumptions would be needed for inference beyond this sample.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Update a claim when evidence changes — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Empty sample: reject an undefined mean.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Deliver a cautious quantitative brief

A mean describes observed values; it does not guarantee the next observation. A range communicates variation without proving a calibrated forecast. Keep descriptive results, uncertainty assumptions and causal claims separate. State observed facts, assumptions and what remains unknown. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values.

Worked example

HANDOFF MANIFEST:
input.json — immutable source
expected.json — fixture answer key
actual.json — result actually observed
evidence-log.csv — baseline plus three changes
README.md — reproduction and access route
limitations.md — unresolved failures, rights, approvals and scope
Never bundle a credential.

Your practical task

1. State observed facts, assumptions and what remains unknown.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Deliver a cautious quantitative brief — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Repeating the same five observations does not create independent new evidence.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Capstone self-review

Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment.

• Defined task and preserved input: Original input and the project policy are retained; the actual question is clear.
• Checked baseline: The supplied baseline reconciles field by field; unsupported live-run claims are absent.
• Three failure/change tests: All three specified changes have predictions, observed results and explanations.
• Repeatable handoff: Another person can follow the README; supporting practice evidence and unresolved limits are included.
• Safety, rights and access honesty — critical: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled.

Review questions

Practice files

• README.md
• evidence-log.csv
• expected.json
• failure-tests.md
• hosted-lab.ipynb
• input.json
• lab_engine.py
• reference.py
• requirements.txt

References and editorial limits

Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated.

• Python statistics documentation

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