What percentage of inspectors would have ‘failed’ to ‘make the cut’ assuming the industry standard for acceptable inspection accuracy and speed combined was set at 95%? How frequently were different levels of inspection accuracy and speed observed? What was the shape of the distribution of inspection accuracy and speed scores? What was the range of accuracy and speed scores the lowest and the highest scores? What was the most common accuracy and speed score amongst the inspectors? What was the typical level of accuracy and decision speed for inspectors in the sample? Consider the following questions that Maree might wish to address with respect to decision accuracy and speed scores: Reflect on the QCI research scenario and the associated data set discussed in Chap. What remains after their application is simply for us to interpret and tell the story. These statistical procedures are designed to identify or display specific patterns or trends in the data. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Along the way, we explore the fundamental concepts of probability and the normal distribution. a histogram, box plot, radar plot, stem-and-leaf display, icon plot or line graph) or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement (e.g., frequency counts, measures of central tendency, variability, standard scores). By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data (e.g. The purpose of the procedures and fundamental concepts reviewed in this chapter is quite straightforward: to facilitate the description and summarisation of data. This chapter discusses and illustrates descriptive statistics.