ABSTRACT
This chapter describes how sampling error is created and the concept of standard error of mean and the central limit theorem. It shows how the sample size and variability in a population affect the standard error of mean. The chapter discusses the 95% or 99% confidence interval and its interpretation. Although bias has been eliminated through use of random sampling, sampling error created by the random selection process may affect the results. To understand the effects of random sampling error on results in the long run, consider a researcher who drew not just one random sample but many such samples from the population that has a mean of 80. It is important to keep in mind that confidence intervals are valid only when the means are obtained with random sampling. Confidence intervals assist in interpreting means that are subject to random errors; they cannot take bias into account.
