What is Simple Random Sample?

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A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.

Definition

Simple random sampling refers to a subset of a statistical population where each member has an equal probability of being chosen. It implies an unbiased representative sampling of a group.

Origin

The concept of simple random sampling originates from the foundational theories of statistics, dating back to the late 19th and early 20th centuries when statisticians began systematically studying how to draw samples from populations for inference. As statistics evolved, simple random sampling became a standard sampling method.

Categories and Features

There are two main forms of simple random sampling: sampling with replacement and sampling without replacement. Sampling with replacement means that after a sample is drawn, it is returned to the population and can be selected again in subsequent draws; sampling without replacement means once a sample is selected, it is not returned to the population. The key feature of simple random sampling is that each sample unit has an equal chance of being selected, ensuring the sample's representativeness and unbiasedness.

Case Studies

Case Study 1: In a national survey, researchers used simple random sampling to select 1,000 participants from the entire population, ensuring that each individual had an equal chance of being chosen, thus obtaining a representative survey result. Case Study 2: A company conducting an employee satisfaction survey used simple random sampling to randomly select 200 employees from all staff to participate in the questionnaire, ensuring the fairness and accuracy of the survey results.

Common Issues

Common issues include insufficient sample size leading to lack of representativeness and the practical difficulty of achieving complete randomization. Solutions include increasing the sample size and using computer-generated random numbers to enhance the degree of randomization.

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