Random Sampling

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What is Random Sampling?

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Random sampling is a method of selecting a subset of individuals from a larger population, where each individual has an equal chance of being chosen.

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Why is Random Sampling important?

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Random sampling is important because it helps ensure that the sample represents the population, reducing bias and allowing for more accurate conclusions.

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What is a Non-Random Sample?

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A non-random sample is a selection of individuals that does not give every member of the population an equal chance of being chosen, which can lead to biased results.

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Example of Random Sampling: Polling 100 random 7th graders about their science class.

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This is a random sample because every 7th grader has an equal chance of being selected.

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Example of Non-Random Sampling: Asking 50 people in a grocery store about their favorite store.

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This is a non-random sample because it only includes people who are currently in the grocery store, not the entire population.

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What is a Convenience Sample?

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A convenience sample is a type of non-random sampling where individuals are selected based on their easy availability.

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What is a Systematic Sample?

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A systematic sample is a type of random sampling where you select every nth individual from a list or queue.

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Example of Systematic Sampling: Asking every 10th person entering a mall about their shopping habits.

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This is a systematic sample because it follows a specific interval (every 10th person) for selection.

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What is a Stratified Sample?

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A stratified sample is a type of random sampling where the population is divided into subgroups (strata) and random samples are taken from each subgroup.

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Why might a researcher use Stratified Sampling?

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A researcher might use stratified sampling to ensure that specific subgroups are adequately represented in the sample.