In this talk an old concept of McIntyre (1952) on {\it ranked set sample} ($RSS$) and its uses for statistical inference will be explored. In situations where the experimental or sampling units in a study can be more easily ranked than quantified, McIntyre (1952) proposed that the mean of $n$ units based on a {\it ranked set sample} ($RSS$) be used to estimate the population mean, and observed that it provides an unbiased estimator with a smaller variance compared to the usual sample mean based on a simple random sample ($SRS$)of the same size $n$. McIntyre's concept of $RSS$ is essentially nonparametric in nature in that the underlying population distribution is assumed to be completely unknown. We will examine McIntyre's results and further explore the concept of $RSS$ when the population is partially known. Specific results for a normal distribution will be given. The technical tool throughout the lecture will be some interesting properties of order statistics.