Stratified sampling and quota sampling are both methods used to ensure that specific subgroups within a population are adequately represented in a sample. In stratified sampling, the population is divided into distinct strata or groups based on specific characteristics such as age, gender, or income level. A random sample is then drawn from each stratum proportionally or equally, depending on the research design. This method enhances representativeness and reduces sampling error. In contrast, quota sampling is a non-probability sampling technique where the researcher identifies relevant strata and sets a target number (quota) for each group. Participants are then selected non-randomly until each quota is filled, often based on convenience. While quota sampling is quicker and easier to implement, it may introduce bias due to the lack of random selection, making stratified sampling generally more reliable for statistical inference.
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