What is sampling in research methodology? Why is having a representative sample important? <\/p>\n\n\n\n
Many research and survey projects rely on sampling as a way to learn about a larger population. Researchers aim to select samples that reflect the target population as closely as possible. <\/p>\n\n\n\n
Keep reading for the definition of a representative sample, why it’s important, and how to collect samples that are representative of the population in question. <\/p>\n\n\n\n\n\n\n\n
The definition of a representative sample is in the name: A sample that accurately represents<\/em> the population in question. If the data collected in our sample doesn’t accurately represent our population, then our resulting statistics will be unreliable. <\/strong>There are two main ways to ensure a representative sample:<\/p>\n\n\n\n Random Sampling: <\/strong>A truly random sample is ideal for data collection. Random sampling allows us to be reasonably confident that we’re capturing the diversity of the underlying population because any individual has as much chance as any other of being selected. Therefore, the diversity of the sample should be close to the diversity of the population. When a sample accurately reflects the composition of its population, it’s referred to as a \u201crepresentative sample.”<\/strong><\/p>\n\n\n\n Large Sample Sizes: <\/strong>The larger our sample size, the greater the likelihood that it will represent the underlying population. This is in part because a larger sample size means more chances for the inclusion of diversity and in part because a larger sample reduces the influence of outliers.<\/strong> As a rule, the larger the sample, the more reliable the statistics. However, Wheelan reminds us that this rule only applies to a truly random sample. A biased sample, big or small, will produce biased statistics.<\/p>\n\n\n\n