Random sampling

Definition

Random sampling is a method of selecting a subset of individuals from a population in which each member has an equal chance of being chosen. This technique aims to produce representative and unbiased samples.

5 Must Know Facts For Your Next Test

1. Random sampling minimizes selection bias, ensuring each member of the population has an equal probability of inclusion.
2. It is essential for statistical inference, allowing results to be generalized from the sample to the population.
3. Common random sampling techniques include simple random sampling, stratified random sampling, and systematic sampling.
4. The law of large numbers states that as the sample size increases, the sample mean will get closer to the population mean.
5. Random sampling requires a complete list (sampling frame) of all members in the population.

Review Questions

• Why is random sampling important in statistical analysis?
• What are three common techniques used in random sampling?
• How does increasing sample size affect the accuracy of a random sample?

Related terms

Simple Random Sampling:

A basic type of random sampling where every member of the population has an equal chance of being selected.

Stratified Random Sampling: A technique where the population is divided into subgroups (strata) and random samples are taken from each stratum.

Systematic Sampling:

A method where every k-th member of the population is selected after arranging them in some order.