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Sampling Methods definitions

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  • Population
    Entire group of individuals or items under study, from which samples are drawn for statistical analysis.
  • Sample
    Subset of a population selected for analysis, used to make inferences about the whole group.
  • Sampling Frame
    List or database containing all elements of a population, serving as the basis for selecting samples.
  • Random Sampling
    Technique where each member of the population has an equal chance of being chosen, minimizing bias.
  • Stratified Sampling
    Method dividing the population into subgroups and selecting samples from each, ensuring representation.
  • Cluster Sampling
    Approach where groups or clusters are randomly chosen, and all or some members within are studied.
  • Systematic Sampling
    Procedure involving selection of every nth item from a list, starting from a random point.
  • Convenience Sampling
    Non-random method relying on easily accessible subjects, often leading to less representative samples.
  • Quota Sampling
    Technique where researchers ensure certain characteristics are proportionally represented in the sample.
  • Sampling Bias
    Distortion in results caused by non-random selection or exclusion of certain population segments.
  • Nonresponse
    Situation where selected individuals do not participate, potentially affecting the accuracy of results.
  • Sampling Error
    Difference between sample results and true population values, arising from chance selection.
  • Simple Random Sample
    Subset chosen so every possible group of a given size has an equal probability of selection.
  • Voluntary Response
    Sampling method where participants self-select, often leading to overrepresentation of strong opinions.
  • Representative Sample
    Subset reflecting the characteristics of the population, allowing for valid generalizations.