The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.

The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.

The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

How the most important statistical method used in many of the sciences doesn't pass the test for basic common sense

"Clinical versus Statistical Prediction" is Paul Meehl's famous examination of benefits and disutilities related to the different ways of combining information to make predictions. It is a clarifying analysis as relevant today as when it first appeared. A major methodological problem for clinical psychology concerns the relation between clinical and actuarial methods of arriving at diagnoses and predicting behavior. Without prejudging the question as to whether these methods are fundamentally different, we can at least set forth the obvious distinctions between them in practical applications. The problem is to predict how a person is going to behave: What is the most accurate way to go about this task? "Clinical versus Statistical Prediction" offers a penetrating and thorough look at the pros and cons of human judgment versus actuarial integration of information as applied to the prediction problem. Widely considered the leading text on the subject, Paul Meehl's landmark analysis is reprinted here in its entirety, including his updated preface written forty-two years after the first publication of the book. This classic work is a must-have for students and practitioners interested in better understanding human behavior, for anyone wanting to make the most accurate decisions from all sorts of data, and for those interested in the ethics and intricacies of prediction. As Meehl puts it, " "When one is dealing with human lives and life opportunities, it is immoral to adopt a mode of decision-making which has been demonstrated repeatedly to be either inferior in success rate or, when equal, costlier to the client or the taxpayer.""

Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies.