Representative & Random Sampling

A representative sample is a subset of a population that accurately reflects the characteristics of the whole population.

Random sampling is a technique where each member of a population has an equal chance of being selected for the sample.

Random sampling is important because it helps to eliminate bias and ensures that the sample is representative of the population.

A sample is a smaller group selected from a population, which is the entire group of individuals or items being studied.

Sampling bias occurs when certain members of a population are more likely to be selected for the sample than others, leading to an unrepresentative sample.

You can create a random sample by assigning each student a number and using a random number generator to select students.

Stratified sampling is a method where the population is divided into subgroups (strata) and random samples are taken from each stratum.

Systematic sampling involves selecting every nth member from a list of the population after a random starting point.

A large enough sample size is important to ensure that the results are statistically significant and can be generalized to the population.

Convenience sampling is a non-random method where samples are taken from a group that is easily accessible.