A sample refers to a small representative portion or subset of a larger whole. It is taken or selected from a larger population or group to gather information, study, or analyze characteristics that are assumed to be similar to the entire population.
In statistics, sampling plays a crucial role in drawing conclusions about an entire population without examining every individual unit.
The word “sample” has its origins in the Middle English word “sampler,” which is derived from the Old French word “essampler,” meaning “to show” or “to present.” The term evolved over time to its current form in English, reflecting the action of taking a part of something to represent the whole.
Imagine a market research company conducting a survey about people’s coffee preferences in a city with a population of one million. To avoid the cost and time of surveying all one million people, the researchers take a sample of 1,000 individuals. This sample, although small, is carefully chosen to represent the broader population’s coffee preferences accurately.
FAQs (Frequently Asked Questions)
What is the purpose of sampling in statistics?
Sampling in statistics is used to draw conclusions about a larger population without studying every individual unit. It allows researchers to make inferences and predictions with a smaller representative subset, saving time, effort, and resources.
How do researchers select a representative sample?
Researchers use various sampling techniques, such as random sampling, stratified sampling, and cluster sampling, to ensure that every member of the population has an equal chance of being included.
What are the advantages of using sampling in research?
Sampling offers several advantages, including cost-effectiveness, time efficiency, ease of data collection, and reduced data processing. It enables researchers to obtain reliable results without having to study the entire population.
Can a small sample size be sufficient for accurate conclusions?
While larger sample sizes generally provide more precise results, a well-designed small sample can still yield reasonably accurate conclusions. The accuracy depends on the sampling technique, the variability of the population, and the desired level of confidence in the results.
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