# Report uncertainty instead of fake precision Open Practice / Planning & uncertainty / curriculum 1.0 Estimated guided practice: 4h estimated. This estimate has not been learner-tested. Original practice material · CC BY 4.0 · Non-degree and non-accredited. ## What you will build Deliver a cautious quantitative brief with inputs, tests and a reproducible handoff. ## Before you start - 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. ## Learning outcomes - Distinguish a count and a mean from a future prediction. - Use minimum, maximum and range to reveal what a mean hides. - If simulating, record seed and assumptions rather than calling synthetic draws real observations. - Compare the before/after mean without inventing a cause. - Keep a reproducible result and disclose safety, rights and regional access limits. ## Activity 1: Describe the observed sample Estimated time: 35 minutes. ### Outcomes - Distinguish a count and a mean from a future prediction. - Explain and retain a testable result and its limits. 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 ```text SOURCE (authored fixture): { "values": [ 2, 4, 4, 6, 9 ] } INDEPENDENT FIXTURE TARGET: { "count": 5, "mean": 5, "minimum": 2, "maximum": 9, "range": 7 } ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## Activity 2: Calculate spread by hand Estimated time: 35 minutes. ### Outcomes - Use minimum, maximum and range to reveal what a mean hides. - Explain and retain a testable result and its limits. 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 ```text 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. ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## Activity 3: Explore a reproducible simulation Estimated time: 35 minutes. ### Outcomes - If simulating, record seed and assumptions rather than calling synthetic draws real observations. - Explain and retain a testable result and its limits. 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 ```text 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 } ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## Activity 4: Stress-test an outlier Estimated time: 35 minutes. ### Outcomes - Compare the before/after mean without inventing a cause. - Explain and retain a testable result and its limits. 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 ```text 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. ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## Activity 5: Update a claim when evidence changes Estimated time: 35 minutes. ### Outcomes - Explain which assumptions would be needed for inference beyond this sample. - Explain and retain a testable result and its limits. 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 ```text 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. ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## Activity 6: Deliver a cautious quantitative brief Estimated time: 65 minutes. ### Outcomes - State observed facts, assumptions and what remains unknown. - Explain and retain a testable result and its limits. 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 ```text 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. ``` ### Guided activity 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. ### Expected evidence - 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. ### Common mistakes - 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? ### 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. ## End-of-path review These are self-check questions, not verified examinations. ### Question 1 For the supplied fixture, what is the expected count? 1. 5 2. It cannot be checked against the supplied input. 3. Whatever value the model reports most confidently. Review answer: 1. The independent answer key only validates this constructed fixture. Preserve the source, rerun counterexamples, and disclose what was not tested. ### Question 2 How should you handle the project rule: Compute count, mean, minimum and maximum for the supplied fictional observations. No confidence-interval or population-representativeness claim is established by these five values. 1. Silently change the rule to fit an attractive output. 2. State the rule, test it, and record exceptions or unresolved failures. 3. Treat model confidence as proof. Review answer: 2. Rules, source provenance and counterexamples make an output inspectable; an attractive result does not override policy. ### Question 3 If mainstream providers do not serve your region, what is the honest route? 1. Use a false country or borrowed account. 2. Claim the supplied fixture was a real API run. 3. Check legitimate available alternatives, or label the manual/fixture route and its limits. Review answer: 3. Access includes country, identity, language, cost and data terms. Do not bypass restrictions or invent a live result. ## Capstone review rubric 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. ## References - [Python statistics documentation](https://docs.python.org/3/library/statistics.html) ## Editorial boundary Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated. Use fictional/redacted data and your own authorised provider credentials. Never include credentials in this pack or public work.