Start by deciding on the population that you want to study. There are 4 key steps to select a simple random sample. You divide the sample into clusters that approximately reflect the whole population, and then choose your sample from a random selection of these clusters. Cluster sampling is appropriate when you are unable to sample from the entire population.You split your population into strata (for example, divided by gender or race), and then randomly select from each of these subgroups. Stratified sampling is appropriate when you want to ensure that specific characteristics are proportionally represented in the sample.It can also be used when you don’t have a complete list of the population. Systematic samplinginvolves choosing your sample based on a regular interval, rather than a fully random selection.In some cases, it might be more appropriate to use a different type of probability sampling: Simple random sampling works best if you have a lot of time and resources to conduct your study, or if you are studying a limited population that can easily be sampled. You have the time and resources to collect data from the necessary sample size.You can contact or access each member of the population if they are selected.You have a complete list of every member of the population.To use this method, there are some prerequisites: However, simple random sampling can be challenging to implement in practice.
In addition, with a large enough sample size, a simple random sample has high external validity: it represents the characteristics of the larger population. It helps ensure high internal validity: randomization is the best method to reduce the impact of potential confounding variables. Simple random sampling is used to make statistical inferences about a population. Frequently asked questions about simple random sampling.Officials from the United States Census Bureau follow a random selection of individual inhabitants of the United States for a year, asking detailed questions about their lives in order to draw conclusions about the whole population of the US. ExampleThe American Community Survey (ACS) uses simple random sampling. Because it uses randomization, any research performed on this sample should have high internal and external validity, and be at a lower risk for research biases like sampling bias and selection bias. This method is the most straightforward of all the probability sampling methods, since it only involves a single random selection and requires little advance knowledge about the population.
In this sampling method, each member of the population has an exactly equal chance of being selected.
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Try for free Simple Random Sampling | Definition, Steps & ExamplesĪ simple random sample is a randomly selected subset of a population. non-probability samplingĮliminate grammar errors and improve your writing with our free AI-powered grammar checker.
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