A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18-29, 30-39, 40-49, 50-59, and 60 and above. To stratify this sample, the researcher Simple random sampling. Simple random sampling is a type of probability sampling technique [see our article, Probability sampling, if you do not know what probability sampling is]. With the simple random sample, there is an equal chance (probability) of selecting each unit from the population being studied when creating your sample [see our article, Sampling: The basics, if you are unsure Course: AP®︎/College Statistics > Unit 6. Lesson 3: Random sampling and data collection. Techniques for generating a simple random sample. Simple random samples. Techniques for random sampling and avoiding bias. Systematic random sampling. Sampling methods. Sampling method considerations. A simple random sample is a subset of a statistical population in which each member has an equal probability of being chosen. Learn how to conduct a simple random sample using methods like lotteries or random draws, and what factors can affect its accuracy and bias. See examples of simple random samples in science, business, and finance. Multistage sampling of 4 items from 3 blocks. Multistage sampling divides large populations into stages to make the sampling process more practical. A combination of stratified sampling or cluster sampling and simple random sampling is usually used. Watch the video for an overview of multistage sampling, examples, plus advantages and disadvantages: Sampling without replacement is the method we use when we want to select a random sample from a population. For example, if we want to estimate the median household income in Cincinnati, Ohio there might be a total of 500,000 different households. Thus, we might want to collect a random sample of 2,000 households but we don't want the data Random sampling is also used for other sampling techniques such as stratified sampling. Stratified sampling requires another sampling method such as a simple random sample to generate a random selection of data values once the data is divided into subgroups (or subsets).This means that each item of data has an equal probability of being chosen and each subgroup within the sample is represented An important benefit of simple random sampling is that it allows researchers to use statistical methods to analyze sample results. For example, given a simple random sample, researchers can use statistical methods to define a confidence interval around a sample mean. Statistical analysis is not appropriate when non-random sampling methods are used. A simple random sample is a randomly selected subset of a population. It is the most straightforward and easiest method of probability sampling, since it only involves a single random selection and requires little advance knowledge about the population. Learn how to perform simple random sampling, when to use it, and see an example from the American Community Survey. The most common sampling designs are simple random sampling, stratified random sampling, and multistage random sampling. Simple Random Sampling Simple random sampling is the basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each 1TsQ.