Since a sample will be used to draw conclusions about the population, the sample must be a representative sample. Keep reading in order to see what we mean by that!
In other words, it must not be biased. Instead, it must represent the entire population as closely as possible.
Among others, statisticians could follow the following guidelines to ensure that the sample is a representative sample.
First, they can ensure that the survey is not a self-selected survey.
In a self-selected survey, the participants volunteer themselves to answer the questions.
Usually, when people volunteer themselves, they may have strong opinions about the matter.
Suppose that candidates A, B, and C are applying for the position of superintendent in a school district.
A sample of 500 people is selected to answer questions about the candidates.
What will happen if 100 of those participants are closely related to candidates B and volunteer themselves to take the survey?
It is likely that the survey will be biased and not represent the population correctly.
Moreover, the sample cannot be too small. Imagine choosing a sample of 200 participants from a population of 10,000,000.
It is likely that this sample will not represent the population correctly.
Not only, the sample must be large enough, the response rate cannot be too low.
For example, a sample of 5000 people was chosen from a population of 100,000.
The sample size looks really good. However, if only 30 people completed the survey, the response rate is too low.
Sometimes, the wrong people are selected to represent the population. There are many ways this could happen.
Whether or not the sample is representative of the population, different studies can still produce different results.