Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.

This book provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It attempts to establish a gold standard for how a statistical analysis should proceed. The book illustrates relative belief theory using many examples and describes the strengths and weaknesses of the theory. The author also addresses fundamental statistical issues, including the meaning of probability, the role of subjectivity, the meaning of objectivity, and the role of infinity and continuity.

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Aimed at students and researchers in statistics and in the medical and health care sector as well as those who use and assess medical data, this work addresses common pitfalls in experimental design, focusing on the errors and misleading data that stem from flawed experiments and analytical methods in medical research.

With increasing frequency, the proof of facts in legal proceedings en tails the use of quantitative methods. Judges, lawyers, statisticians, social scientists, and many others involved in judicial processes must address is sues such as the evaluation and interpretation of quantitative evidence, the ethical and professional obligations of expert witnesses, and the roles of court-appointed witnesses. The Panel on Statistical Assessments as Evi dence in the Courts was convened to help clarify these issues and provide some guidance in addressing the difficulties encountered in the use of quan titative assessments in legal proceedings. This report is the culmination of more than three years of research and deliberation. In it, we address a variety of issues that arise in federal and state court proceedings when statistical assessments such as quantitative descriptions, causal inferences, and predictions of events based on earlier occurrences are presented as evidence. We appraise the forms in which such assessments are presented, aspects of their admission into evidence, and the response to and evaluation of them by judges and juries.

Mark Taper, Subhash Lele and an esteemed group of contributors explore the relationships among hypotheses, models, data and interference on which scientific progress rests in an attempt to develop a new quantitative framework for evidence.

Statistics for Evidence-Based Practice in Nursing, Second Edition presents statistics in a readable, user-friendly manner for both graduate students and the professional nurse.