ABSTRACT
As important as sample size is, it is secondary to the larger questions of composition and bias. To understand why bias is more important than sample size, consider this example: A student is conducting a survey on whether the main cafeteria on campus should open during evening hours. The sample could easily be biased against those who are on campus in the evenings, and those are the students most likely to use the cafeteria in evening hours. It can be tricky to identify biases based on differences between those who respond and those who do not, but researchers may compare the sample with the known parameters of the population to establish the degree to which the sample is representative of the population. Once researchers address potential biases, sample size is the next consideration. It is important to note that precision does not necessarily mean a lack of bias.
