Stefanie Markovits’ chapter thinks about counting and accountability, and how they inform literary representations of the military man, one of the most visible of war’s outcomes in mid-Victorian Britain. Markovits reflects on this period as one which saw ‘the rise of statistics as a discipline of social science and a method of statecraft in Britain’, and with it the growing need for accountability in public affairs. The figure of the soldier is both hero and statistic, individual and number, in a period where fiction, philosophy, and popular commentary, were preoccupied with how individuals realised their fully individualised potential. The soldier’s cultural and political potency is enabled because his being is aligned with the numbers that account for him. In the work of Tennyson, Harriet Martineau, and Dickens we see how ‘the mid-century soldier becomes such a potent figure precisely because his “type” aligns so closely with numbers’.

Three Stanford-educated Chicanos took the stand in support of MAS, and these witnesses were central in Judge Tashima’s final ruling. Specifically, they detailed in a scholarly way the academic integrity of the department, the efficacy of taking the classes, and also demonstrated how state representatives used racist “code words” in cementing their opposition to the program. We detail their times testifying, how the state desperately tried to trip them up.

This chapter links the creation of MAS to the historical creation of Ethnic Studies – setting the record straight on the nature of this type of education amidst massive amounts of local and national misinformation. It details what MAS was, the effects of the program on student academic success, while examining how critically engaged, educated Mexican American students came to be seen as such a “threat” to the state.

This chapter is written for conversation analysts and is methodological. It discusses, in a step-by-step fashion, how to code practices of action (e.g., particles, gaze orientation) and/or social actions (e.g., inviting, information seeking) for purposes of their statistical association in ways that respect conversation-analytic (CA) principles (e.g., the prioritization of social action, the importance of sequential position, order at all points, the relevance of codes to participants). As such, this chapter focuses on coding as part of engaging in basic CA and advancing its findings, for example as a tool of both discovery and proof (e.g., regarding action formation and sequential implicature). While not its main focus, this chapter should also be useful to analysts seeking to associate interactional variables with demographic, social-psychological, and/or institutional-outcome variables. The chapter’s advice is grounded in case studies of published CA research utilizing coding and statistics (e.g., those of Gail Jefferson, Charles Goodwin, and the present author). These case studies are elaborated by discussions of cautions when creating code categories, inter-rater reliability, the maintenance of a codebook, and the validity of statistical association itself. Both misperceptions and limitations of coding are addressed.

The goal of public health is to improve the overall health of a population by reducing the burden of disease and premature death. In order to monitor our progress towards eliminating existing problems and to identify the emergence of new problems, we need to be able to quantify the levels of ill health or disease in a population. Researchers and policy makers use many different measures to describe the health of populations. In this chapter we introduce more of the most commonly used measures so that you can use and interpret them correctly. We first discuss the three fundamental measures that underlie both the attack rate and most of the other health statistics that you will come across in health-related reports, the incidence rate, incidence proportion (also called risk or cumulative incidence) and prevalence, and then look at how they are calculated and used in practice. We finish by considering other, more elaborate measures that attempt to get closer to describing the overall health of a population. As you will see, this is not always as straightforward as it might seem.

This chapter considers the role of neuropsychology in the diagnostic process. It covers who can undertake a neuropsychological assessment, when to undertake an assessment, and some of the assumptions underlying neuropsychological assesssment. Basic psychometrics are covered, using the premise that undertanding a few basic concepts is sufficient for most practioners as more complex ideas are developed from these basics. This includes the normal distribution, different types of average, the standard deviation, and the correlation. Next, the relationship between different tyes of metrics is discussed, focusing on IQ/Index scores, T-scores, scaled scores, and percentiles.

Chapter 4 examines efforts in the nineteenth and early twentieth centuries to better understand Britain’s rain. Meteorologists had attempted to investigate the distribution of rain prior to the 1850s, but observation points remained few and inadequately distributed. The solution to answering questions about the geographies of the rain was the establishment of a rainfall observatory network that covered the entirety of the British Isles. The network of rainfall observing stations was established by George Symons and became known as the British Rainfall Organisation. It relied almost exclusively on volunteer labour. The first section of the chapter details the early years of Symons’s Rainfall Organisation and its key administrative features, before moving on to discussions about rain gauges and station exposures. The chapter then examines a series of experimental trials that ran from 1863 to 1890 and discusses the ensuing controversy regarding the value of the experiments and of the observatory network more generally. The chapter then looks at contemporary discussions about the value of various statistical treatments of rain data, before finishing with Alexander Buchan’s and Hugh Robert Mill’s rainfall maps and the maps’ contributions to data management and public utility.

This chapter describes the basics of scientific figures. It provides tips for identifying different types of figures, such as experimental protocol figures, data figures, and summary figures. There is a description of ways to compare groups and of different types of variables. A short discussion of statistics is included, describing elements such as central tendency, dispersion, uncertainty, outliers, distributions, and statistical tests to assess differences. Following that is a short overview of a few of the more common graph types, such as bar graphs, boxplots, violin plots, and raincloud plots, describing the advantages that each provides. The end of the chapter is an “Understanding Graphs at a Glance” section which gives the reader a step-by-step outline for interpreting many of the graphs commonly used in neuroscience research, applicable independently of the methodology used to collect those data.

Network science is a broadly interdisciplinary field, pulling from computer science, mathematics, statistics, and more. The data scientist working with networks thus needs a broad base of knowledge, as network data calls for—and is analyzed with—many computational and mathematical tools. One needs good working knowledge in programming, including data structures and algorithms to effectively analyze networks. In addition to graph theory, probability theory is the foundation for any statistical modeling and data analysis. Linear algebra provides another foundation for network analysis and modeling because matrices are often the most natural way to represent graphs. Although this book assumes that readers are familiar with the basics of these topics, here we review the computational and mathematical concepts and notation that will be used throughout the book. You can use this chapter as a starting point for catching up on the basics, or as reference while delving into the book.

This chapter introduces the reader to facial recognition technology (FRT) history and the development of FRT from the perspective of science and technologies studies. Beginning with the traditionally accepted origins of FRT in 1964–1965, developed by Woody Bledsoe, Charles Bisson, and Helen Wolf Chan in the United States, Simon Taylor discusses how FRT builds on earlier applications in mug shot profiling, imaging, biometrics, and statistical categorisation. Grounded in the history of science and technology, the chapter demonstrates how critical aspects of FRT infrastructure are aided by scientific and cultural innovations from different times of locations: that is, mugshots in eighteenth-century France; mathematical analysis of caste in nineteenth-century British India; innovations by Chinese closed-circuit television companies and computer vision start-ups conducting bio-security experiments on farm animals. This helps to understand FRT development beyond the United States-centred narrative. The aim is to deconstruct historical data, mathematical, and digital materials that act as ‘back-stage elements’ to FRT and are not so easily located in infrastructure yet continue to shape uses today. Taylor’s analysis lays a foundation for the kinds of frameworks that can better help regulate and govern FRT as a means for power over populations in the following chapters.