Review a Sample Justification

You are an expert M&E sampling specialist reviewing the sample size and selection rationale for "program or study" (from the material, if given).

SAMPLE JUSTIFICATION TO REVIEW: the attached or pasted document

Review Requirements:

1. Analytic purpose match. Verify that the sample size supports the intended analyses, including planned disaggregation (e.g., sex, age, region, treatment status) and any subgroup comparisons. Flag cases where the overall total is treated as sufficient without checking cell sizes.
2. Statistical justification. Where quantitative inference is intended, check that confidence level, margin of error or detectable effect size, expected variability or baseline rate, design effect for clustered sampling, and a non-response buffer are stated. Flag missing assumptions.
3. Qualitative saturation logic. Where qualitative inference is intended, check that the sample is sized for saturation, information power, or another theoretically grounded reason rather than a round-number convention.
4. Selection method fit. Assess whether the selection approach (probability, purposive, theoretical) matches the target population and the evaluation question. Flag mismatches (e.g., purposive used where representativeness is claimed).
5. Feasibility. Judge whether the sample is achievable given access, time, budget, and field conditions. Flag samples that are statistically defensible but operationally unrealistic.

If the material is incomplete: Work only from the sample justification section and study details provided. Where an assumption needed for the review, such as the confidence level, expected variability, or design effect, is missing or unstated, say so under that requirement and note how it limits your assessment, then continue with the rest. Do not invent statistical assumptions, figures, or content the document does not state.

Output Format:

Produce:

1. A 1-paragraph overall assessment.
2. A scored review table by dimension (analytic purpose match, statistical justification, qualitative saturation, selection method fit, feasibility), with score (1-5), evidence, and recommended action.
3. A prioritized revision list (must-fix vs. should-fix).
4. A short note on whether the sample as proposed will support the analyses the document claims it will support.

Where the material is silent, write "Not in the material" rather than guessing.

Document to review: Attached, or pasted below. Base everything above on it and do not invent details that are not in it.