This volume begins with an essay by Ruth A. Wienclaw that offers a sweeping overview of the underlying concepts and uses of statistics. While she acknowledges that “statistics allow one to organize and interpret data that would otherwise be incomprehensible,” she also notes that for statistics to be useful, “one must recognize… that there is an underlying uncertainty and variability in data and collections of data.” She continues by outlining some of the errors that can negatively impact results and by stressing the importance of choosing correct techniques before observing how statistics only yield probabilities, not exact results. Two specific types of statistics, as discussed by Wienclaw, are descriptive statistics and inferential statistics. Individuals and business often use descriptive statistics to help organize and describe data. Inferential statistics, on the other hand, are most often used in the analysis of data and allow people to develop conclusions about the data and the coinciding statistical significance. An example of a field that employs the use of both types of statistics is engineering. Engineers use descriptive and inferential statistics to test hypotheses, help with product design, and enact quality control measures. One of the common quality control tools is quality control charts. These charts help managers monitor processes to determine whether or not the process is under control. Engineers also use statistics to evaluate, improve, and optimize the processes in a manufacturing system. Proper usage allows organizations to maintain high levels of effectiveness and efficiency.