Sampling

Understanding Sampling

  • Sampling is the process of selecting a small group from a larger population for study or analysis.
  • The small group, known as the sample, should be representative of the population it is taken from.
  • An unbiased sample is one that has been selected randomly so that each individual in the population has an equal chance of being chosen.
  • A biased sample is one where certain groups or individuals are more or less likely to be chosen.

Sampling Techniques

  • Random sampling is when each individual in a population has an equal chance of being selected. This tends to give the most unbiased results.
  • Systematic sampling involves selecting every nth individual from the population (for example, every 10th person).
  • Stratified sampling is used when the population is divided into different subgroups, or strata. A sample is then taken from each subgroup proportional to its size in the population.
  • Quota sampling is similar to stratified sampling, but the sample does not need to be proportionate to the population size.
  • Cluster sampling involves dividing the population into groups, or clusters, and then randomly selecting a number of clusters to sample from.
  • Convenience sampling is when samples are taken from a population that is convenient to the researcher.

Key Considerations in Sampling

  • The sample size must be large enough to be representative of the overall population.
  • Consider possible sampling errors, which can occur when a sample selected does not properly represent the whole population.
  • Non-sampling errors could occur due to mistakes in data collection, recording or analysis.
  • Ensure ethical considerations are followed, particularly with regards to consent and privacy.

Using sampling data

  • Sampling data provides an estimate, or inference, about the population.
  • Researchers may use hypothesis testing to draw conclusions based on the sample data.
  • Analyse data using appropriate statistical techniques including measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation).

Remember, validity and reliability of your sampling process are paramount to ensure the results accurately represent the population.