There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of statistics is design and experimental statistics. Descriptive statistics involve the tabulating, depicting, and describing of collections of data. These data may be either quantitative or qualitative. They provide a picture or description of the properties of data collected in order to summarize them into manageable form. Inferential statistics are a formalized body of techniques that infer the properties of a larger collection of data from the inspection of that collection. They build on these statistics as they infer the properties of samples to various populations. Design and analysis statistics were developed for the discovery and confirmation of causal relationships among variables in social science experiments. They use a variety of statistical tests related to aspects such as prediction and hypothesis testing. Experimental analysis is related to [Page 146]comparisons, variance, and ultimately testing whether variables are significant between each other. The latter two types of statistics are usually either parametric or nonparametric. The importance of statistics in the research process is sometimes exaggerated. Thus, a highly sophisticated statistical analysis rarely, if ever, compensates for a poorly conceived project, a poorly constructed research design, or an inaccurate data collection instrument. Thus, statistics certainly may aid the researcher but are never a substitute for good, sound thinking and attention to the scientific method and research process. For researchers, then, statistics are simply a tool to help them study the phenomena they are interested in.