A population is an entire dataset that we wish to draw the conclusion of. Denoted by “N”. A parameter is a measure that describes the whole population.
A subset of the population is a sample. Denoted by “n”. In general, it is impractical to collect data for the entire population so we have to rely on the sample, which is small, manageable, cost-effective and representative of the whole population. A statistic is a measure that describes the sample.
Sampling Error is the difference between the population parameter and sample statistics. Sampling error can occur due to the random selection of sample (as the sample is not representing the entire population in a better way). In general, the aim is to generalize the findings from the sample to the entire population, hence we need sampling error to be low. One way of doing that is increasing the sample size. Other could be selecting a sample in such a way that it will represent the population
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