The strengths of this book include the directness of its encounter with research data, its advice on practical data analysis issues, careful critiques of analysis results, its use of modern data analysis tools and approaches, its use of simulation and other computer-intensive methods where these provide insight or give results that are not otherwise available, its attention to graphical and other presentation issues, its use of examples drawn from across the range of statistical applications, the links that it makes into the debate over reproducibility in science, and the inclusion of code that reproduces analyses. The methods that we cover have wide application. The datasets, many of which have featured in published papers, are drawn from many different fields. They reflect a journey in learning and understanding, alike for the authors and for those with whom they have worked, that has ranged widely over many different research areas. The R system has brought into a common framework a huge range of abilities for data analysis, data manipulation and graphics. Our text has as its aim helping its readers to take full advantage of those abilities.

In Chapter 1 we define community, consulting, and other concepts that inform and influence our work as community consultants. We explain how community consulting is different from other types of consulting, describe the purpose of the book, and provide guidance for readers in making the most use of the information provided.

Chapter 10 presents an overview of the current state of community consulting, the positive aspects, and what we can expect in the future.

The bulk of medical care system management in the United States does not ground decisions in use of available, high quality evidence. The previous chapters in this book demonstrate both that there sometimes is a rich scientific foundation in health care organization, delivery, and financing that could, if applied, lead to better outcomes, and that sometimes there is little or no evidence on effectiveness of interventions. However, we also observed that the bulk of health care management in the United States does not ground decisions in evidence, using it if available and taking uncertainty into account if not – instead “magical thinking” is often used to make choices.1 Ironically, management holds evidence in high esteem for decision-making by clinicians. In this chapter, we explore why management holds itself to a lower standard regarding its organizational, staffing, and planning choices, seeing experience, intuition, and opinions as good-enough evidence for decisions. We explore what needs to happen for this to change.