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Sampling is a fundamental concept in research, monitoring, and evaluation that involves selecting a subset of individuals, items, or observations from a larger population to draw conclusions about that population. Since it is often not feasible, economical, or even possible to study every individual in a large population, sampling provides a practical solution to generate reliable and valid data within limited resources and time. In the context of monitoring and evaluation of projects and programmes, sampling becomes essential for assessing outcomes, impacts, and effectiveness without the need for a complete census. By carefully designing sampling strategies, evaluators can ensure that findings are representative and credible, thereby supporting evidence-based decision-making. Probability sampling is one of the most rigorous and widely used approaches in evaluation research. In probability sampling, every unit in the population has a known, non-zero chance of being selected. This feature distinguishes it from non-probability sampling and ensures that results can be generalized to the entire population with measurable accuracy. The strength of probability sampling lies in its ability to reduce selection bias and allow for statistical inference, which is particularly valuable when evaluating programmes that seek to demonstrate accountability and effectiveness. There are several types of probability sampling methods, each suited to specific research designs, population structures, and evaluation needs. The first and most fundamental method is simple random sampling. In this method, each member of the population has an equal chance of being included in the sample. This technique is often implemented by using random number tables, lottery systems, or computer-generated random selections. For ___ ______ ____ _____ _______ ___ _______ _____ _____ _______ ____ _____.
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