Sampling Plan Draft

---

name: sampling-plan-draft description: Draft an M&E sampling plan, or check an existing one's sizing and selection, from purpose, population, frame and constraints, with transparent calculations and unresolved decisions. Use when a survey or evaluation needs a sample design.

Sampling plan drafting

Purpose and trigger

Use when a practitioner needs a prospective sampling plan for monitoring, a baseline, a survey or an evaluation. The output is an editable Markdown design draft, not a claim that respondents were selected or data collected. For a supplied plan, use the same method to check design consistency without replacing the user's approved choices. This file owns the method; supporting examples illustrate it.

Status: Checked in Claude Code. Anthropic's help page says Free and paid plans can use uploaded skills once code execution is turned on; we checked browser upload on a paid plan only. The files are free; Claude account access and usage limits are separate. No API key or site signup is required by these instructions.

Dates: Never write today's date, the system date, or words such as "passed", "late" or "stale" about a supplied date in the artifact. Supplied dates govern; if a date's status matters, write "status unknown from supplied files".

Minimum input and intake

Supply the decision or study purpose, indicator or question, target population/unit and what is known about the sampling frame. Include precision or comparison requirements, proposed mode, geography/time, required subgroups, resources and access if available. A short description is acceptable; a complete TOR is not mandatory.

• Browser: upload readable source attachments. Do not request machine paths or search folders. If an attachment cannot be read, ask for accessible text and identify what remains unassessed.
• Code: use explicit files or the user's scoped folder. List candidate files when discovery is needed. Do not search unrelated folders; already named files need no repeated approval.
• Record filenames, versions/dates and page/section/table or row references. Show supplied facts, unresolved conflicts, assumptions and proposed design choices separately. Missing in the input does not mean nonexistent or unapproved elsewhere.
• No identifiable M&E sampling purpose: return a suitability note and stop. With purpose but missing population/frame, produce a bounded design outline and exact decisions needed; do not calculate a representative sample.

Reproducible sequence

1. Define the claim and population. Name the estimand or qualitative question; eligibility/exclusions, counting and selection units, geography, reference dates and intended inference population. Distinguish enrolled people, households, records and encounters. State whether the job is descriptive estimation, comparison/change detection, causal evaluation or qualitative explanation. Random sampling alone does not identify a causal effect; causal identification and random assignment are different design questions.
2. Assess the frame. Describe its source/date, unique IDs, coverage, omissions, duplicates, eligibility errors, contactability and access. A larger sample cannot repair coverage bias. A phone list excludes people without listed working phones unless corrected. Offer frame repair or an explicitly narrower population as proposed options, not a silent scope change. Do not expose actual personal identifiers in the plan.
3. Choose an approach with rationale. Compare a census, simple random, stratified, systematic, cluster/multistage or nonprobability approach as relevant. Known nonzero inclusion probabilities and implementable selection are needed for design-based population inference. Probability sampling can start from area frames; a missing name list is not automatically a reason to use convenience sampling. Systematic selection needs random start, a valid interval/procedure and a periodicity check. Purposive qualitative selection needs explicit inclusion/variation/access logic; do not attach a survey margin of error to it. Census attempts can still have nonresponse and measurement error.
4. Size only the applicable design. Use the rules below and reproduce inputs, formula, substitutions, rounding, units and source of each parameter. Distinguish completed eligible responses from invitations/contacts. Independently recompute supplied and proposed arithmetic using an available calculator, or step-by-step manual substitution when none is available; state how it was checked and show discrepancies from the original. Do not merely repeat a supplied result. If a calculation cannot actually be verified, label it unchecked and leave the sizing verdict unresolved. Never fabricate a tool run or certified precision. Missing design inputs mean scenarios or a sizing specification, not a confirmed number.
5. Specify selection and field rules. Explain each selection stage, frame freeze/cleaning, reproducible randomization procedure and seed to be recorded at execution, without claiming it already happened. Define contact attempts/spacing, consent/refusal and ineligibility recording, no pressure to participate, and a predeclared reserve-list procedure if used. Do not replace refusals with whoever is available. Preserve original selection and disposition records. Distinguish invitation inflation, differential response and coverage; extra invitations do not remove response bias.
6. Check analysis and feasibility. Identify design weights and inclusion probabilities, nonresponse adjustment assumptions, survey-design-aware variance and required domain precision. Do not promise small subgroups are precise because total n is sufficient or because each exceeds 30. For clustered/panel designs name PSU/strata/repeated-unit effects and dependencies. Compare contacts, completed responses, cluster counts, timing and effort against supplied resources only. A budget cap can require redesign or reduced precision, not false certification of an underpowered design.
7. Plan validation and handoff. Propose a pilot/frame audit, what it would check, decision criteria and who must authorize changes. Label all proposed roles, dates, thresholds and permissions. Include limitations, alternatives, unresolved decisions, conditions before fieldwork and a change record. Do not report hypothetical pilot findings as observations.

Sizing rules and boundaries

Single proportion, simple random sampling without replacement

Only when the request is a two-sided approximate confidence interval for one proportion under simple random sampling:

n0 = z^2 * p * (1-p) / e^2

If a known finite eligible population N is relevant and the sampling fraction warrants correction, use n = n0 / (1 + (n0 - 1)/N). Otherwise retain n0 and explain why. Round required completions up once after adjustment; then invitations = ceil(required completions / r) for a supplied or explicitly hypothetical response fraction r. These approximations assume the chosen confidence method is suitable; rare outcomes or boundary proportions may require another interval/design.

• z, p, absolute margin e, N and response fraction r need sources or explicit scenario labels. For example, e=0.05 means five percentage points, not five percent of p. p=0.5 maximizes p(1-p) for an absolute-margin single-proportion scenario; it is not a universal conservative setting for rare outcomes, relative precision, means or power.
• Do not silently use 95%, five percentage points, 80% power, a design effect or an 80% response rate as approved inputs. If the user asks for a scenario, label every assumed parameter and keep it separate from confirmed requirements. If the frame is unreconciled or a required design input (such as response rate, eligibility rate or intraclass correlation) is missing and the user did not ask for scenarios, present the calculation table as a checklist of required inputs and do not compute sample sizes.
• Validate 0<p<1, 0<e<1, 0<r<=1, positive z, integer N>0 and compatible units. An invitation count exceeding N is infeasible under a without-replacement eligible frame: report it and revise requirements/design, rather than quietly capping it and retaining the promised precision.
• This expression does not size a mean, rare-event detection, paired change, clustered comparison, impact study, LQAS classification or subgroup domain. Do not reuse it for those purposes.

Other designs

• Comparison/power: require outcome type, comparison design, effect/MDE, variance or baseline rates, significance/test direction, power, allocation, repeated measures/attrition and clustering as applicable. Supply a design-specific calculation specification and seek statistical review where necessary; do not certify power from a generic proportion formula.
• Cluster/multistage: identify clusters, stages, selection probabilities and within-cluster correlation/design-effect evidence. A design effect is estimator/design specific. 1 + (m-1)*ICC is a simplified equal-cluster approximation, not a universal formula. Unknown ICC, unequal sizes or too few feasible clusters remain design problems; multiplying n by an arbitrary default does not solve them. Finite corrections and nonresponse must be applied at the appropriate stage, with statistical review for complex designs.
• Stratified/domain estimates: size and allocate for the actual reporting domains and precision requirements. Proportional allocation can leave small domains unusable; oversampling changes weights. Report tradeoffs without inventing subgroup targets.
• Qualitative: specify purposive criteria, variation, recruitment, initial proposed range if requested, analysis-informed stopping/review rules and limits. Do not promise saturation at a fixed interview count or represent frequency as population prevalence.

Output structure

For drafting mode, use the structure below. For an existing-plan consistency check, keep scope and source register, compare the supplied choices/calculations to the same design rules and return evidence-linked issues plus proposed corrections. Do not silently replace the plan or call proposed changes approved. A missing plan means no full check verdict.

Write one Markdown artifact with:

1. Scope, decision/use and status (draft, supplied approval or unresolved).
2. Source/evidence register, supplied facts and conflicts.
3. Population, frame quality, intended inference and exclusions.
4. Recommended approach and alternatives, explicitly proposed.
5. Calculation table or unresolved sizing specification; assumptions and verified arithmetic.
6. Stage-by-stage selection, contacts/nonresponse and data protection.
7. Analysis/weights/domain precision and limitations.
8. Feasibility, pilot, unresolved decisions and conditions before fieldwork.
9. Completion check and change record.

When checking an existing plan, additionally retain ## Critical Issues, ## Important Issues and ## Minor Improvements, even when an item is None identified or Not assessable. Do not conflate this targeted consistency check with a completed independent review of every sampling criterion.

Completion and resumption

• Every requested indicator/domain is covered or explicitly unresolved.
• Frame limitations and inference claims agree; sampling and causal identification are separate.
• Completions/invitations, periods, assumptions and arithmetic reconcile. No invented targets, observations, costs, references, permissions or commitments.
• Proposed selection can be implemented; privacy, access, nonresponse, weights and field validation are addressed or flagged.
• Inspect the actual saved artifact if file tools are available. A chat summary does not replace a promised file. Without file creation, return editable Markdown and state no downloadable file was created.
• On interruption save actual drafts, calculations, source/version register and a short handover listing next stage and decisions. On resume compare inherited claims against original sources; classify each as confirmed, contradicted or unconfirmed. Preserve unknown status instead of converting it into absence or approval. Recalculate affected results after any changed parameter and record the change.

Public methodological context

This is a bounded design aid, not a replacement for design-specific statistical work. General sampling, frames and nonsampling error are covered in the UN Statistics Division's Designing Household Survey Samples: Practical Guidelines. Complex survey analysis must reflect the design; see the CDC's NHANES variance estimation tutorial. These references are background, not claims that a user's proposed design meets their requirements. Do not invent page citations or claim to have opened a source unless actually read.