stratified sample
a sample that is chosen by dividing the population into homogenous groups and then choosing the same amount (either count or percentage) from each group
Stratified sampling is a statistical sampling technique that involves dividing a population into subgroups or strata, based on some common characteristic that is relevant to the study. Random samples are then taken from each stratum in proportion to its size, ensuring that each subgroup is adequately represented in the final sample. This process is used to increase the accuracy and precision of estimates by reducing the sampling error that can occur due to chance variations in the sample.
For example, if we want to conduct a study on the effectiveness of a new drug on a population of adults, we can divide the population into subgroups based on gender, age, or medical history. We can then randomly sample individuals from each subgroup, ensuring that each subgroup is represented in proportion to its size in the population. This will help us get more accurate estimates of the drug’s effectiveness on different subgroups, taking into account any differences that may exist between them.
Stratified sampling can be more effective than simple random sampling because it reduces the variability within subgroups, making it easier to detect differences between them. It also allows researchers to make more accurate estimates about the population as a whole, by weighting the results from each subgroup according to its size in the population. However, stratified sampling requires careful planning and can be more time-consuming than simple random sampling, especially if there are many subgroups to consider.
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