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Sampling is a systematic process of selecting a subset of individuals, items, or observations from a larger population with the objective of drawing conclusions about the entire population. In the context of monitoring and evaluation of projects and programmes, sampling becomes an essential tool because it is often impractical, costly, and time-consuming to collect data from every individual within the target group. Through appropriate sampling methods, evaluators can obtain reliable information that represents the larger population, ensuring both efficiency and accuracy. Sampling reduces the burden of exhaustive data collection while maintaining validity and reliability, and it provides a basis for making generalizations, assessing progress, identifying gaps, and supporting evidence-based decision-making. Sampling is broadly divided into probability sampling and non-probability sampling. Probability sampling ensures that every unit in the population has a known and non-zero chance of being selected. This feature makes probability sampling more scientifically robust because it reduces bias and allows the use of statistical inference. In monitoring and evaluation, probability sampling is often preferred when the aim is to assess impact, measure outcomes, or produce results that can be generalized to the entire population. The main probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Each method has its own procedures, advantages, and limitations depending on the project or programme context. Simple random sampling is the most basic form of probability sampling where every unit in the population has an equal chance of being selected. This is often implemented using random number tables, lottery _______ ______ _______ ____ ____ _______.
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