In the previous section, Random Sampling, you learned that some sampling methods were biased, and can produced samples unrepresentative of the population from which they are selected. In the next activity, you will need to be able to distinguish between a sample and a population and identify different sampling methods. Prepare for that now by formally defining the population and sample, then learning about four common sampling methods.

Sampling Methods

In a moment, you’ll learn about four sampling methods commonly used in research. It is important to note that not all statistical questions can be investigated using purely unbiased methods.  Therefore, acknowledging bias in the sampling method is crucial to interpreting the analysis. Before we introduce these methods, let’s formally define what we mean by populations and samples.

Population vs. Sample

In the previous section, Random Sampling, you identified populations and parameters from descriptions of studies. The population was the entire group of interest and the parameter was a numerical summary describing a characteristic of that group.

In real life, it is usually hard to collect data on an entire population. Therefore, samples are used in research studies to collect data to make inferences about a population. Collecting data from a sample is less time consuming and less costly than collecting data from an entire population.

Answer Questions 1 and 2 now to define the terms population and sample.