The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This 2002 book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference during the 1920s and 1930s. Through the examination of a dispute between two British scientists, the author argues that a choice between the two interpretations is not forced by pure logic or the mathematics of the situation, but depends on the experiences and aims of the individuals involved. The book should be of interest to students and scientists interested in statistics and probability theories and to general readers with an interest in the history, sociology and philosophy of science.

What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.

Presenting a realistic interpretation of quantum mechanics and, in particular, a realistic view of quantum waves, this book defends, with one exception, Schrodinger's views on quantum mechanics. Johansson goes on to defend the view that the collapse of a wave function during a measurement is a real physical collapse of a wave and argues that the collapse is a consequence of quantisation of interaction. Lastly Johansson argues for a revised principle of individuation in the quantum domain and that this principle enables a sort of explanation of non-local phenomena.

Interpreting Biomedical Science: Experiment, Evidence, and Belief discusses what can go wrong in biological science, providing an unbiased view and cohesive understanding of scientific methods, statistics, data interpretation, and scientific ethics that are illustrated with practical examples and real-life applications. Casting a wide net, the reader is exposed to scientific problems and solutions through informed perspectives from history, philosophy, sociology, and the social psychology of science. The book shows the differences and similarities between disciplines and different eras and illustrates the concept that while sound methodology is necessary for the progress of science, we cannot succeed without a right culture of doing things. - Features theoretical concepts accompanied by examples from biological literature - Contains an introduction to various methods, with an emphasis on statistical hypothesis testing - Presents a clear argument that ties the motivations and ethics of individual scientists to the success of their science - Provides recommendations on how to safeguard against scientific misconduct, fraud, and retractions - Arms young scientists with practical knowledge that they can use every day

Until now, Ghelly Chernov’s work on the theory of simultaneous interpretation (SI) was mostly accessible only to a Russian-speaking readership. Finally, Chernov’s major work, originally published in Russia in 1987 under the title Основы Синхронного Перевода (Introduction to Simultaneous Interpretation) and widely considered a classic in interpretation theory, is now available in English as well. Adopting a psycholinguistic approach to professional SI, Chernov defines it as a task performed in a single pass concurrently with the source language speech, under extreme perception and production conditions in which only a limited amount of information can be processed at any given time. Being both a researcher and a practitioner, Chernov drew from a rich interpreting corpus to create the first comprehensive model of simultaneous interpretation. His model draws on semantics, pragmatics, Russian Activity Theory and the SI communicative situation to formulate the principles of objective and subjective redundancy and identify probability prediction as the enabling mechanism of SI. Edited with notes and a critical foreword by two active SI researchers, Robin Setton and Adelina Hild, this book will be useful to practicing interpreters in providing a theoretical basis for appreciating the syntactic and other devices that can be used by both students and experienced interpreters in fine-tuning their performance in the booth.

This book describes the basis, application, and interpretation of statistics, and presents a wide range of univariate and multivariate statistical methodology. The Second Edition retains the unique feature of being written from the users' perspective; it connects statistical models and methods to investigative questions and background information, and connects statistical results with interpretations in plain English. In keeping with this approach, methods are grouped by usage rather than by commonality of statistical methodology.

"Interpreting Economic and Social Data" aims at rehabilitating the descriptive function of socio-economic statistics, bridging the gap between today's statistical theory on one hand, and econometric and mathematical models of society on the other. It does this by offering a deeper understanding of data and methods with surprising insights, the result of the author's six decades of teaching, consulting and involvement in statistical surveys. The author challenges many preconceptions about aggregation, time series, index numbers, frequency distributions, regression analysis and probability, nudging statistical theory in a different direction. "Interpreting Economic and Social Data" also links statistics with other quantitative fields like accounting and geography. This book is aimed at students and professors in business, economics demographic and social science courses, and in general, at users of socio-economic data, requiring only an acquaintance with elementary statistical theory.

Interpreting Statistics for Beginners teaches readers to correctly read and interpret results of basic statistical procedures as they are presented in scientific literature, and to understand what they can and cannot infer from such results. The first of its kind, this book explains key elements of scientific paradigms and philosophical concepts that the use of statistics is based on and introduces readers to basic statistical concepts, descriptive statistics and basic elements and procedures of inferential statistics. Explanations are accompanied with detailed examples from scientific publications to demonstrate how the procedures are used and correctly interpreted. Additionally, Interpreting Statistics for Beginners shows readers how to recognize pseudoscientific claims that use statistics or statements not based on the presented data, which is an important skill for every professional relying on statistics in their work. Written in an easy-to-read style and focusing on explaining concepts behind statistical calculations, the book is most helpful for readers with no previous training in statistics, and also those wishing to bridge the conceptual gap between doing the statistical calculations and interpreting the results.

The Art and Science of Interpreting Market Research Evidence offers a complete account of the way today's researchers interpret evidence and apply it to decision making. David Smith and Jonathan Fletcher show how to assess your current deciphering processes, and present an innovative framework integrating quantitative and qualitative approaches for analysing complex data-sets. With its holistic approach to interpretation and its 10-step process for making it work in practice, this book will equip you with a deep understanding of data analysis and ultimately improve your judgment to produce better business decisions. "This is modern commercial research, where the mind of the researcher is finally acknowledged as admissible data. Prior knowledge, pragmatism, experience are all robust grist to the 'holistic' research mill. A must-read for anyone getting to grips with 21st century market research." Virginia Valentine, Semiotic Solutions

Interpreting Complex Forensic DNA Evidence is a handy guide to recent advances—and emerging issues—in interpreting complex DNA evidence and profiles for use in criminal investigations. In certain cases, DNA cannot be connected to a specific biological material such as blood, semen or saliva. How or when the DNA was deposited may be an issue. The possibility of generating DNA profiles from touched objects, where there may not be a visible deposit, has expanded the scope and number of exhibits submitted for DNA analysis. With such advances, and increasing improvements in technological capabilities in testing samples, this means it is possible to detect ever smaller amounts of DNA. There are also many efforts underway to seek was to interpret DNA profiles that are sub-optimal—either relative to the amount required by the testing kit and, potentially, the quality of the obtained sample. Laboratories often use enhancements in order to obtain a readable DNA profile. The broad-reaching implications of improving DNA sensitivity have led to this next, emerging generation of more complex profiles. Examples partial profiles that do not faithfully reflect the proposed donor, or mixtures of partial DNA from multiple people. A complexity threshold has been proposed to limit interpretation of poor-quality data. Research is now addressing the interpretation of transfer of trace amounts of DNA. Complex issues are arising in trial that need to be reconciled as such complexity has added challenges to the interpretation of evidence and its introduction or dismissal in certain cases in the courts. Key Features: Addresses DNA transfer, from person-to-person as well as to objects Outlines each stage required to produce a DNA profile from an exhibit—including collection, handling, storage, and analysis Discusses ethics, subjectivity, and bias—including cognitive dissonance—as they relate specifically to complex DNA evidence Highlights current techniques and the latest advances in DNA analysis, including advances in familial DNA searches Interpreting Complex Forensic DNA Evidence provides tools to assist the criminal investigator, forensic expert, and legal professional when posed with a DNA result in a forensic report or testimony. The result—and any associated statistic—may not reveal any ambiguity, complexity, or the assumptions involved in deriving it. Questions from resolved criminal cases are posed, and the relevant forensic literature, provided for the reader to assess a DNA result and any associated statistic. Case studies throughout illustrate concepts and emphasize the need for conclusions in the forensic report that are supported by the data.