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Efficient health care requires informed doctors and patients. The health care system inherited from the 20th century falls short on both counts. Many doctors and most patients do not understand the available medical evidence. Seven “sins” are identified which have contributed to this lack of knowledge: biased funding; biased reporting in medical journals; biased patient pamphlets; biased reporting in the media; conflicts of interest; defensive medicine; and medical curricula that fail to teach doctors how to comprehend health statistics. These flaws have generated a partially inefficient system that wastes taxpayers’ money on unnecessary or even potentially harmful tests and treatments as well as on medical research that is of limited relevance to the patient. Raising taxes or rationing care is often seen as the only viable alternative to exploding health care costs. Yet there is a third option: by promoting health literacy, better care is possible for less money. The 21st century should become the century of the patient. Governments and health institutions need to change course and provide honest and transparent information to enable better doctors, better patients, and, ultimately, better health care.

Studies consistently show that health care practitioners are poor at processing statistical information on risk. Indeed, almost everybody in health care, apart from a few people with advanced training, is poor at processing statistical information. The inability to process statistical information among health care practitioners is one cause of patients failing to receive optimal care. But there are many others, including a flawed and biased information base, unawareness of evidence, unwillingness to accept the evidence, lack of applicability of the evidence, inability to implement treatments, failures to act, unwillingness of patients to accept evidence, and failure to adhere to treatments. Commissioners of health care and those who set policy may be in a much stronger position than practitioners to follow evidence, as they have access to expert advice and are not under the same time pressures as practitioners. The reductionist ideas of evidence-based medicine, however, are not simply applied in commissioning and the making of policy. Improvement is most likely to come with a systems approach, making it easy for practitioners to use interventions based on evidence and more difficult to use those not based on evidence.

At a time when financial pressures and a digital revolution threaten the survival of many media outlets, a focus on statistical literacy can improve health and medical reporting and perhaps foster survival-enhancing changes in how the media cover these topics. Journalists often lack knowledge about statistical thinking. First reports of scientific findings, advances in “hot” research fields, and results that contradict previous assumptions draw special attention from the media, but underlying statistical problems and uncertainties in such studies are rarely mentioned in news stories. Clinical trials, significance testing, and meta-analyses create particular confusion for journalists. Possible ways to remedy this problem include early statistical education, professional development courses, online assistance sites, and efforts to use personal stories to illuminate the predictive value of medical tests.

Many doctors, patients, journalists, and politicians alike do not understand what health statistics mean, or they draw wrong conclusions without noticing. The causes of statistical illiteracy should not be attributed to cognitive biases alone, but to the emotional nature of the doctor–patient relationship and conflicts of interest in the health care system. As the chapter shows, many sources of medical information intentionally or uninentionally use nontransparent information to persuade individuals, with serious consequences for personal health. Without understanding the numbers involved, the public is susceptible to political and commercial manipulation of their anxieties and hopes, which undermines the goals of informed consent and shared decision making.

Statistical illiteracy in health—the inability to understand health statistics—is widespread among the general public. Many people find it hard to accept uncertainty in the first place and, even if they do, basic numerical information is difficult to understand. The problem is aggravated when the benefits and harms of treatment options must be evaluated or test outcomes understood. Statistical illiteracy results not only from a lack of education but from the nontransparent framing of information that is sometimes unintentional, but which can also be used deliberately to manipulate people. In health care, nontransparent framing of information seems to be the rule rather than the exception. Patients have difficulties finding reliable information—on the Internet, in invitations to screening, medical pamphlets, or media reports—yet this situation can be corrected. Statistical thinking must be taught to the public, and health care workers and journalists must be trained in transparent framing. Knowing what questions to ask, what information is missing, and how to translate nontransparent statistics into transparent ones would empower an educated citizenry to reject attempts to persuade rather than inform.

In most psychological, legal, and medical research, patients are assumed to have difficulties with health statistics but clinicians not. Doctors may be said to pay insufficient attention to their patients’ feelings, not listen carefully to their complaints, take only an average of five minutes time for consultations, or withhold information; yet rarely is it considered that they might also be statistically illiterate. However, studies indicate that most doctors have problems in understanding health statistics, including those from their own area of specialty. Such statistical illiteracy makes informed shared decision making impossible. The reasons for these disconcerting findings appear to be less rooted in doctors’ cognitive limitations but rather in medical schools that ignore the importance of teaching risk communication. Doctors could be taught, with little effort, the simple techniques of risk communication, which would eliminate most of their statistical confusion.

The UK-based Radical Statistics Group has a long-standing role in shaping statistics to support progressive social change. It has worked to demystify and critique official statistics, and to trace the consequences of using statistical models and their assumptions The Group has used its energies to encourage statistical literacy and campaigning effectiveness among progressive groups that seek its help. Its early days from the 1970s were characterised by a range of ‘progressive’ publications and well-received interventions in crucial debates and official consultations. In the 1990s it contributed to the wave of reforms of statistical outputs and procedures brought to fruition by the incoming Labour government. At the current time it provides ongoing resources of annual conferences, regular journal and email, a website and social media. Campaigns are often developed outside Radical Statistics structures, but with the key support of RadStats contacts, resources and ideas. At a time when it is archiving its first forty years of papers in the Welcome Library, Radical Statistics envisages a future enhanced by the activity of a range of allies, and the resources they provide, so as to formulate effective alternatives to the dominant discourses of our time.

Large amounts of data, relevant to decision making and political argument, are now available. However, these data are often accessible only to people with reasonably developed skills in data acquisition and exploration; less skilled users must depend on interpretations by others. This chapter shows how large amounts of evidence relevant to decision making can be made accessible to a broad public, via software the authors have developed and made widely available. The Constituency Explorer resulted from a collaboration between the House of Commons Library and Durham University, and was designed to support analysis and decision making in the 2015 and 2017 UK general elections. It facilitates access to 150 variables for each of the 650 parliamentary constituencies in the UK, which can be explored in an interactive way. The authors describe the design and features of the interface, and some of the ways it has been used. Finally, they outline some strategies for public engagement which include ‘gamification’ via a quiz accessible to smartphones.

An important theme in an introductory statistics course is the connection between statistics and the outside world. Described in this chapter are assignments that can be useful in getting students to learn how to gather and process information presented in the news and scientific reports. These assignments seem to work well only when students have direction about how to do this kind of research. Three versions of the assignment are provided. In all three, students read a news story and the original report on which the article was based, and they complete a worksheet with guidelines for summarizing the reported study. In some versions students are supplied the news story and report and in another each student finds a news article and tracks down the original report on her own. Included here are our guidelines, example instructional packets, and the process we use to organize each type of assignment.

Quantitative analyses are a powerful tool in the human rights practitioner’s methodology toolbox. Statistics allow researchers to reframe and examine topics in order to provide context or insights different from the information gathered in qualitative interviews, with the most common uses of data analysis being to demonstrate the scope, distribution (over geography and/or time), or variance of a human rights problem. Because numbers demand attention, there is increasing motivation to utilize data and statistics in human rights fact-finding. However, practitioners risk using methodologies or techniques inappropriately, either because of pressure to use new techniques they do not fully understand or to use a known methodology when another technique would be more appropriate. Increasing human rights practitioners’ quantitative literacy is the first step to guard against inappropriate or ineffective use of data and statistics. Practitioners need to develop quantitative literacy so as to understand the methodologies used to gather data.

This chapter, “Numeracy’s Secret Connection with Life Outcomes,” reviews available evidence for numeracy’s relations with outcomes in health, employment, and personal finances. Less numerate people tend to be less healthy than the highly numerate, with a 40% greater likelihood, for example, of having at least one chronic disease. Numeracy skills also are required for many jobs, ranging from engineering and science to service and sales workers, but research on this topic is limited. Numeracy strongly predicts financial behaviors and wealth, over and above general intelligence. Although people say “I’m not a math person” as casually as “I hate broccoli,” math ability appears to play a critical role in their well-being.

This chapter, “Issues and Opportunities in Objective Numeracy Research,” discusses three cross-cutting questions in objective numeracy research. The first two issues concern the correlational nature of most objective numeracy research. Alternative explanations exist for the effects of numeracy on decisions and life outcomes. In particular, this chapter questions whether general intelligence can explain objective numeracy effects and whether the reverse causal path may offer a better explanation. Objective numeracy generally emerges as the better explanation, but alternative explanations remain of some effects (e.g., worse health sometimes may produce lower numeracy). The third issue concerns researchers’ experimental design decisions and what these results might teach us about how to improve numeric reasoning in decisions.

This chapter, “The Types and Extent of Innumeracy,” briefly describes three kinds of numeric competency. First, people can score high or low on tests of their understanding and use of mathematical concepts (called objective numeracy). Objective innumeracy occurs across levels of education and is not the same as intelligence; very smart people can be quite poor with numbers in critical ways. Second, people can be good or bad at persisting in numeric tasks because of their subjective numeric confidence. Despite existing objective innumeracy, 63% of Americans nonetheless say that understanding medical statistics is easy. Finally, people differ in an intuitive number sense that appears to underlie childhood development of objective numeracy and to compensate for adults’ low objective numeracy abilities. The Appendix for this chapter introduces measures of objective numeracy and subjective numeracy.