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  1. M&E Library
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  3. Topics
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  5. Sampling
Topic Hub

Sampling

Sampling determines whether your data can actually answer the questions you are asking. Get the sample size wrong and you waste money on a survey that cannot detect real change. Choose the wrong sampling method and your findings do not represent the population you claim to serve. This hub covers the full set of sampling decisions: how many people, selected how, with what adjustments for clustering and non-response, and the common mistakes that undermine even well-designed studies. The Sampling Calculator handles the math; these pages explain the judgment calls behind it.

How Do I Choose?

Side-by-side comparisons, decision trees, and practical guidance for common M&E decisions.

How to Choose Sample Size for M&E
A practical guide to sample size for program evaluations, with rules of thumb, worked examples, and budget-statistics tradeoffs.
How to Choose
Cluster Sampling vs Stratified Sampling
Cluster sampling saves money when populations are spread out. Stratified sampling ensures subgroup comparisons. When to use each.
Comparison

Interactive Tools

Sampling Calculator
Calculate the sample size you need for surveys, evaluations, and monitoring activities

Reference Library(7 entries)

Overviews

Baseline Design
A structured approach to collecting initial condition data that directly informs project decisions, minimizes burden, and enables valid comparison with endline measurements.
Disaggregation
The breakdown of aggregate data by sub-group characteristics, such as sex, age, location, or vulnerability status, to reveal inequities and differences in programme reach and outcomes.
Sampling Methods
Systematic approaches for selecting a subset of a population to represent the whole, balancing statistical validity with practical constraints.

Quick Reference

Census vs SampleCluster SamplingPurposive SamplingRandom Sampling

Explore Other Topics

Evaluation
Design, commission, and manage evaluations
MEL Design
Theories of change, logframes, results frameworks, and logic models
Data Collection
Methods, tools, and sampling for field data
Indicators
Select, design, track, and report on indicators
Data Quality
Ensure trustworthy data from collection to analysis