If you wanted to create a more accurate survey that accounted for income levels as an attribute, to compare whether the survey answers varied - relative to income level in that neighborhood - you would have to pick your samples differently, which would take more time. It can leave out some clusters and can't replicate any specific diversity, such as income levels, in the neighborhood. You could divide the neighborhood into streets and randomly select a few of the streets (clusters) to perform your survey.Ĭompared to stratified random sampling, cluster sampling is not as precise and leaves room for error. It may leave out other groups, and the groups it chooses may not accurately represent the whole population.įor example, suppose you wanted to survey a neighborhood’s residents about their political affiliations but didn't have a lot of time. We would have a sample population of 10 people with age subgroups that accurately represent the ratio of age ranges in the target population of 100 people.Ĭluster sampling randomly selects some groups out of a population. If the original population has 40% of under-25-years-olds, 50% of 25–70-years-olds, and 10% over-70-year-olds, we could pick four people who were under 25, five people who were over 25, and one person over 70. Say we want to study a population by its age. This would give a sample that more accurately represents the characteristics of the overall population. You could divide the population into subgroups by a similar attribute such as age, race, gender, income level, etc. If you want a more precise representation of the overall population, you could use stratified random sampling instead. A random sample is easier to create than a stratified random sample, but it may not tell you much about the characteristics of the population. If you have 100 people in a population size and you want a random sample size of 10% of the population, you could put 100 names in a hat, pull out 10 names, and create a simple random sample of 10% of the population. The population is not broken into subpopulations before the random sample is selected. Simple random sampling is a sampling method where every member of the population has an equal chance of being selected. Then we would randomly select 20 lemon ones, and so on, to create our stratified random sample of 100 gummies. In this example, we would randomly select 10 orange gummy bears from the original 100 possible choices to create that stratum of our sample.
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