ABSTRACT
This chapter describes the difference between biased and unbiased sampling. It explores that sampling creates sampling error. The chapter discusses the benefits of unbiased random sampling and how a simple random sampling is done. When populations are large, researchers usually use a sample. The nurses would constitute the population. Populations yield parameters. No matter how a sample is drawn, it is always possible that the statistics obtained by studying the sample do not accurately reflect the population parameters that would have been obtained if the entire population had been studied. Bias exists whenever some members of a population have a greater chance of being selected for inclusion in a sample than other members of the population, creating a biased sample. Simple random sampling identifies an unbiased sample. While using large samples helps to limit the amount of random error, it is important to note that selecting a large sample does not correct for errors due to bias.
