Question
What is sample? Describe the different sampling.
Answer :
Word Count : 1117
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Skip in 10
In research methodology, especially in population and family health studies, a sample refers to a subset of individuals, households, or units selected from a larger population for the purpose of investigation. The population includes all individuals or elements that share certain characteristics relevant to the research problem, such as all married women of reproductive age in a district, all children under five years in a state, or all households in a rural community. Since it is often impractical, costly, and time-consuming to study the entire population, researchers select a representative sample that reflects the essential characteristics of the population. The findings obtained from the sample are then generalized to the larger population with a known level of confidence and precision. Sampling is the process of selecting a portion of the population in such a way that it accurately represents the whole. The validity and reliability of research findings largely depend on how appropriately the sample is chosen. In population and family health studies, careful sampling is crucial because such studies often inform public health policies, family welfare programs, fertility regulation strategies, maternal and child health services, and disease control initiatives. Sampling methods are broadly classified into two categories: probability sampling and non-probability sampling. Probability sampling is a method in which each unit of the population has a known and non-zero chance of being selected. This method is scientifically rigorous and allows the use of statistical tools to estimate sampling error and make valid inferences about the population. Simple random sampling is the most basic form of probability sampling. In this method, every unit in the population has an equal chance of being selected. A sampling frame, which is a complete list of all units in the population, is required. Selection can be done using random number tables, lottery methods, or computer-generated random numbers. For example, if a researcher wants to study contraceptive use among 1,000 eligible couples and selects 100 couples using a random number generator, each couple has an equal probability of selection. This method minimizes selection bias but may not always be feasible in large or geographically dispersed populations. Systematic sampling involves selecting every kth unit from a list after choosing a random starting point. The sampling interval (k) is determined by dividing the population size by the
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