Definition
A random sample is a subset of individuals chosen from a larger set, or population, in such a manner that each member of the population has an equal probability of being included in the sample. This method ensures that the sample fairly represents the population without bias. Random sampling is fundamental in statistics because it lays the foundation for unbiased and generalizable results in research.
Examples
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Survey Research: A company conducting a customer satisfaction survey selects 1,000 customers at random from a database of 50,000 customers to ensure that the responses accurately reflect the views of the entire customer base.
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Clinical Trials: Researchers selecting participants for a clinical trial on a new medication ensure that participants are randomly chosen from the eligible population to avoid selection bias and to ensure that the sample is representative.
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Market Research: A polling organization selects random households across a city to understand the voting preferences of the entire city’s population.
Frequently Asked Questions (FAQs)
What is the difference between a random sample and a simple random sample?
A random sample refers to any sampling method that gives each member of the population an equal chance of being selected. A simple random sample is a type of random sample where each subset of the population has an equal chance of being selected as well.
Why is random sampling important?
Random sampling is crucial because it eliminates selection bias, ensuring that the sample represents the target population. This enhances the accuracy and generalizability of the research findings.
How do you generate a random sample?
A random sample can be generated using various techniques, including:
- Random number tables
- Random number generators (software)
- Lottery methods (drawing names from a hat)
What are the advantages of random sampling?
- Minimizes selection bias
- Enhances the generalizability of results
- Simplifies the application of inferential statistical techniques
What are the limitations of random sampling?
- May be impractical for large populations
- Requires a complete and up-to-date list of the population (sampling frame)
- Pure random selection may not represent sub-groups adequately
Related Terms
- Sampling Frame: The list or database from which the sample is drawn.
- Systematic Sample: A type of non-random sample where elements are chosen at regular intervals from an ordered list.
- Stratified Sample: A sampling method that divides the population into subgroups (strata) and samples are taken from each one.
- Cluster Sample: A sampling method where the population is divided into clusters, and a random sample of these clusters is then selected.
- Probability Sampling: Techniques that give each member of the population a quantum-based probability of selection.
Online References
- National Institute of Standards and Technology (NIST) — Random Sampling
- Statistics How To — Simple Random Sample
- Research Methods Knowledge Base — Probability Sampling
Suggested Books for Further Study
- “The Practice of Statistics for Business and Economics” by David S. Moore, George P. McCabe, Layth C. Alwan
- “Sampling Techniques” by William G. Cochran
- “Statistics for Business and Economics” by Paul Newbold, William L. Carlson, Betty Thorne
- “An Introduction to Statistical Methods and Data Analysis” by R. Lyman Ott, Micheal T. Longnecker
- “Probability and Statistics for Engineers and Scientists” by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye
Fundamentals of Random Sample: Statistics Basics Quiz
Thank you for exploring the concept of random samples. This understanding is pivotal in ensuring the accuracy and generalizability of statistical findings in various fields of study. Keep engaging with the material to cement your knowledge.
