These essays critically reassess the widely accepted view that statistical methods of analysis can, and do, yield causal understanding of social phenomena. They emphasize the historical, philosophical and conceptual perspectives that underlie and inform current methodological controversies.

This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and (...) made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

The author finds no support for the claim that J. M. Keynes had severe reservations, in general, as opposed to particular, concerning the application of mathematical, logical and statistical methods in economics. These misinterpretations rest on the omission of important source material as well as a severe misconstrual ofThe Treatise on Probability (1921).

In this paper, I examine William Whewell’s (1794–1866) ‘Discoverer’s Induction’, and argue that it 21 supplies a strikingly accurate characterization of the logic behind many statistical methods, exploratory 22 data analysis (EDA) in particular. Such methods are additionally well-suited as a point of evaluation of 23 Whewell’s philosophy since the central techniques of EDA were not invented until after Whewell’s death, 24 and so couldn’t have influenced his views. The fact that the quantitative details of some very (...) general 25 methods designed to suggest hypotheses would so closely resemble Whewell’s views of how theories 26 are formed is, I suggest, a strongly positive comment on these views. (shrink)

Science and language; Axioms; Deduction and induction; Probability and statistics; The methodological structure of the natural sciences.

The application of statistical methods and models both in the natural and social sciences is nowadays a trivial fact which nobody would deny. Bold analogies even suggest the application of the same statistical models to fields as different as statistical mechanics and economics, among them the case of the young and controversial discipline of Econophysics . Less trivial, however, is the answer to the philosophical question, which has been raised ever since the possibility of “commuting” (...) class='Hi'>statistical thinking and models between natural and social sciences emerged: whether such a methodological kinship would imply some kind of more profound unity of the natural and the social domain. Starting with Adolphe Quetelet (1796-1874) and ending with the Vienna Circle (from the late 1920s until the 1940s), this paper offers a brief historical and philosophical reconstruction of some important stages in the development of statistics as “commuting” between the natural and the social sciences. This reconstruction is meant to highlight (with respect to the authors under consideration): (1) the existence of a significant correlation between the readiness to “transfer” statistical thinking from natural to social sciences and vice versa, on the one hand, and the standpoints on the issue of the unity/disunity of science, on the other; (2) the historical roots and the fortunes of the analogy between statistical models of society and statistical models of gases. (shrink)

Quantitative researchers often discuss research ethics as if specific ethical problems can be reduced to abstract normative logics (e.g., virtue ethics, utilitarianism, deontology). Such approaches overlook how values are embedded in every aspect of quantitative methods, including ‘observations,’ ‘facts,’ and notions of ‘objectivity.’ We describe how quantitative research practices, concepts, discourses, and their objects/subjects of study have always been value-laden, from the invention of statistics and probability in the 1600s to their subsequent adoption as a logic made to appear (...) as if it exists prior to, and separate from, ethics and values. This logic, which was embraced in the Academy of Management from the 1960s, casts management researchers as ethical agents who ought to know about a reality conceptualized as naturally existing in the image of statistics and probability (replete with ‘constructs’), while overlooking that S&P logic and practices, which researchers made for themselves, have an appreciable role in making the world appear this way. We introduce a different way to conceptualize reality and ethics, wherein the process of scientific inquiry itself requires an examination of its own practices and commitments. Instead of resorting to decontextualized notions of ‘rigor’ and its ‘best practices,’ quantitative researchers can adopt more purposeful ways to reason about the ethics and relevance of their methods and their science. We end by considering implications for addressing ‘post truth’ and ‘alternative facts’ problems as collective concerns, wherein it is actually the pluralistic nature of description that makes defending a collectively valuable version of reality so important and urgent. (shrink)

Through his S–R model of statistical relevance, Wesley Salmon offers a solution to the scientific explanation of objectively improbable events.

Combining information from two or possibly several blocks of data is gaining increased attention and importance in several areas of science and industry. Typical examples can be found in chemistry, spectroscopy, metabolomics, genomics, systems biology and sensory science. Many methods and procedures have been proposed and used in practice. The area goes under different names: data integration, data fusion, multiblock analyses, multiset analyses and a few more. This book is an attempt to give an up-to-date treatment of (...) the most used and important methods within an important branch of the area; namely methods based on so-called components or latent variables. These methods have already obtained an enormous attention in for instance chemometrics, bioinformatics, machine learning and sensometrics and have proved to be important both for prediction and interpretation. The book is primarily a description of methodologies, but most of the methods will be illustrated by examples from the above-mentioned areas. The book is written such that both users of the methods as well as method developers will hopefully find sections of interest. In the end of the book there is a description of a software package developed particularly for the book. This package is freely available in R and covers many of the methods discussed. To distinguish the different type of methods from each other, the book is divided into five parts. Part I is introduction and preliminary concepts. Part II is the core of the book containing the main unsupervised and supervised methods. Part III deals with more complex structures and, finally, Part IV discusses alternative unsupervised and supervised methods. The book ends with Part V discussing the available software. Our recommendation for reading the book are as follows. A minimum read of the book would involve chapters 1, 2, 3, 5 and 7. Chapters 4, 6 and 8 are more specialized and chapters 9 and 10 contain methods we think are more advanced or less obvious to use. We feel privileged to have so many friendly colleagues who were willing to spend their time on helping us to improve the book by reading separate chapters. We would like to express our thanks to: Rasmus Bro, Margriet Hendriks, Ulf Indahl, Henk Kiers, Ingrid MaÌ⁽ge, Federico Marini, AÌ⁽smund Rinnan, Rosaria Romano, Lars Erik Solberg, Marieke Timmerman, Oliver Tomic, Johan Westerhuis and Barry Wise. Of course, the correctness of the final text is fully our responsibility! (shrink)

The advance of science and human knowledge is impeded by misunderstandings of various statistics, insufficient reporting of findings, and the use of numerous standardized and non-standardized presentations of essentially identical information. Communication with journalists and the public is hindered by the failure to present statistics that are easy for non-scientists to interpret as well as by use of the word significant, which in scientific English does not carry the meaning of "important" or "large." This article promotes a new standard (...) method for reporting two-group and two-variable statistics that can enhance the presentation of relevant information, increase understanding of findings, and replace the current presentations of two-group ANOVA, t-tests, correlations, chi-squares, and z-tests of proportions. A brief call to highly restrict the publication of risk ratios, odds ratios, and relative increase in risk percentages is also made, since these statistics appear to provide no useful scientific information regarding the magnitude of findings. (shrink)

On this occasion of honouring the achievement of Philip McShane, I would like to recall his earliest and, in my judgment, most important work, Randomness, Statistics and Emergence. In particular, I will recall how that work situated Lonergan’s important breakthrough on statistical method in relation to the major currents of thought on the subject, many of which remain influential still today.

During the twentieth century statistical methods have transformed research in the experimental and social sciences. Qualitative evidence has largely been replaced by quantitative results and the tools of statistical inference have helped foster a new ideal of objectivity in scientific knowledge. The paper will investigate this transformation by considering the genesis of analysis of variance and experimental design, statistical methods nowadays taught in every elementary course of statistics for the experimental and social sciences. These (...) class='Hi'>methods were developed by the mathematician and geneticist R. A. Fisher during the 1920s, while he was working at Rothamsted Experimental Station, where agricultural research was in turn reshaped by Fisher’s methods. Analysis of variance and experimental design required new practices and instruments in field and laboratory research, and imposed a redistribution of expertise among statisticians, experimental scientists and the farm staff. On the other hand the use of statistical methods in agricultural science called for a systematization of information management and made computing an activity integral to the experimental research done at Rothamsted, permanently integrating the statisticians’ tools and expertise into the station research programme. Fisher’s statistical methods did not remain confined within agricultural research and by the end of the 1950s they had come to stay in psychology, sociology, education, chemistry, medicine, engineering, economics, quality control, just to mention a few of the disciplines which adopted them. (shrink)

Fictional models in science -- The hypothetical versus the fictional -- What is wrong with the new fictionalism of scientific models? -- Re-inflating the conception of scientific representation -- Idealization, confirmation, and scientific realism -- Laws and models in a theory of idealization -- Approximation and its measures -- Approximation, idealization, and the laws of nature -- Coordination of space and unity of science -- Gauge gravity and the unification of natural forces -- Models and theories II: issues (...) and applications -- Models and theories I: the semantic view revisited -- Explaining quantum spontaneous symmetry breaking -- Review: symmetries in physics: philosophical reflections -- Classical spontaneous symmetry breaking -- Spontaneous symmetry breaking and chance in a classical world -- Approximations, idealizations, and models in statistical mechanics -- Infinite systems in SM explanations: thermodynamic limit, renormalization (semi-) groups, and irreversibility -- Explaining the emergence of cooperative phenomena -- A possible unified understanding of probabilistic objects -- Particularism and holism revisited -- Potential, propensity and categorical realism -- The Aharonov-Bohm effect and the reality of wave packets -- Realism and spacetime: of arguments against metaphysical realism and manifold realism -- Gauge invariance, Cauchy problem, indeterminism, and symmetry breaking -- Is there a relativistic thermodynamics? a case study of the meaning of special relativity. (shrink)

Chow's endorsement of a limited role for null hypothesis significance testing is a needed corrective of research malpractice, but his decision to place this procedure in a hypothetico-deductive framework of Popperian cast is unwise. Various failures of this version of the hypothetico-deductive method have negative implications for Chow's treatment of significance testing, meta-analysis, and theory evaluation.

The Philosophy of Quantitative Methods undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.

Basic science -- Experiment -- Null hypothesis statistical testing -- Social science: psychology -- Social science: economics -- Behavioral economics -- "Efficient" markets -- Summing up.

This book applies a range of ideas about scientific discovery found in contemporary philosophy of science to psychology and related behavioral sciences. In doing so, it aims to advance our understanding of a host of important methodological ideas as they apply to those sciences. A philosophy of local scientific realism is adopted in favor of traditional accounts that are thought to apply to all sciences. As part of this philosophy, the implications of a commitment to philosophical naturalism are spelt (...) out, and a correspondence theory of truth is defended by showing how it helps explain various features of scientific practice. The central chapter of the book presents a broad theory of scientific method that comprises the detection of empirical phenomena and their subsequent understanding by constructing explanatory theories through the use of abductive methods. This theory of scientific method is then used as a framework to reconstruct the well-known qualitative method of grounded theory, and to present a systematic perspective on clinical reasoning and case formulation. Relatedly, an abductive or explanationist understanding of methods is employed to evaluate the knowledge credentials of evolutionary psychology. In addition, the conceptual and methodological foundations of a variety of quantitative methods are examined. Exploratory factor analysis and tests of statistical significance are given special attention. (shrink)

This paper recommends how authors of statistical studies can communicate to general audiences fully, clearly, and comfortably. The studies may use statistical methods to explore issues in science, engineering, and society or they may address issues in statistics specifically. In either case, readers without explicit statistical training should have no problem understanding the issues, the methods, or the results at a non-technical level. The arguments for those results should be clear, logical, and persuasive. This (...) paper also provides advice for editors of general journals on selecting high quality statistical articles without the need for exceptional work or expense. Finally, readers are also advised to watch out for some common errors or misuses of statistics that can be detected without a technical statistical background. (shrink)

Over the years, mathematics and statistics have become increasingly important in the social sciences1 . A look at history quickly confirms this claim. At the beginning of the 20th century most theories in the social sciences were formulated in qualitative terms while quantitative methods did not play a substantial role in their formulation and establishment. Moreover, many practitioners considered mathematical methods to be inappropriate and simply unsuited to foster our understanding of the social domain. Notably, the famous Methodenstreit (...) also concerned the role of mathematics in the social sciences. Here, mathematics was considered to be the method of the natural sciences from which the social sciences had to be separated during the period of maturation of these disciplines. All this changed by the end of the century. By then, mathematical, and especially statistical, methods were standardly used, and their value in the social sciences became relatively uncontested. The use of mathematical and statistical methods is now ubiquitous: Almost all social sciences rely on statistical methods to analyze data and form hypotheses, and almost all of them use (to a greater or lesser extent) a range of mathematical methods to help us understand the social world. Additional indication for the increasing importance of mathematical and statistical methods in the social sciences is the formation of new subdisciplines, and the establishment of specialized journals and societies. Indeed, subdisciplines such as Mathematical Psychology and Mathematical Sociology emerged, and corresponding journals such as The Journal of Mathematical Psychology (since 1964), The Journal of Mathematical Sociology (since 1976), Mathematical Social Sciences (since 1980) as well as the online journals Journal of Artificial Societies and Social Simulation (since 1998) and Mathematical Anthropology and Cultural Theory (since 2000) were established. What is more, societies such as the Society for Mathematical Psychology (since 1976) and the Mathematical Sociology Section of the American Sociological Association (since 1996) were founded. Similar developments can be observed in other countries. The mathematization of economics set in somewhat earlier (Vazquez 1995; Weintraub 2002). However, the use of mathematical methods in economics started booming only in the second half of the last century (Debreu 1991). Contemporary economics is dominated by the mathematical approach, although a certain style of doing economics became more and more under attack in the last decade or so. Recent developments in behavioral economics and experimental economics can also be understood as a reaction against the dominance (and limitations) of an overly mathematical approach to economics. There are similar debates in other social sciences. It is, however, important to stress that problems of one method (such as axiomatization or the use of set theory) can hardly be taken as a sign of bankruptcy of mathematical methods in the social sciences tout court. This chapter surveys mathematical and statistical methods used in the social sciences and discusses some of the philosophical questions they raise. It is divided into two parts. Sections 1 and 2 are devoted to mathematical methods, and Sections 3 to 7 to statistical methods. As several other chapters in this handbook provide detailed accounts of various mathematical methods, our remarks about the latter will be rather short and general. Statistical methods, on the other hand, will be discussed in-depth. (shrink)

Winner of the National Business Book Award From the New York Times bestselling author of The Organized Mind and This Is Your Brain on Music, a primer to the critical thinking that is more necessary now than ever We are bombarded with more information each day than our brains can process—especially in election season. It's raining bad data, half-truths, and even outright lies. New York Times bestselling author Daniel J. Levitin shows how to recognize misleading announcements, statistics, graphs, and written (...) reports, revealing the ways lying weasels can use them. It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, and distortions from reliable information? Levitin groups his field guide into two categories—statistical information and faulty arguments—ultimately showing how science is the bedrock of critical thinking. Infoliteracy means understanding that there are hierarchies of source quality and bias that variously distort our information feeds via every media channel, including social media. We may expect newspapers, bloggers, the government, and Wikipedia to be factually and logically correct, but they so often aren't. We need to think critically about the words and numbers we encounter if we want to be successful at work, at play, and in making the most of our lives. This means checking the plausibility and reasoning—not passively accepting information, repeating it, and making decisions based on it. Readers learn to avoid the extremes of passive gullibility and cynical rejection. Levitin's charming, entertaining, accessible guide can help anyone wake up to a whole lot of things that aren't so. And catch some weasels in their tracks! (shrink)

In May of 1973 we organized an international research colloquium on foundations of probability, statistics, and statistical theories of science at the University of Western Ontario. During the past four decades there have been striking formal advances in our understanding of logic, semantics and algebraic structure in probabilistic and statistical theories. These advances, which include the development of the relations between semantics and metamathematics, between logics and algebras and the algebraic-geometrical foundations of statistical theories (especially in (...) the sciences), have led to striking new insights into the formal and conceptual structure of probability and statistical theory and their scientific applications in the form of scientific theory. The foundations of statistics are in a state of profound conflict. Fisher's objections to some aspects of Neyman-Pearson statistics have long been well known. More recently the emergence of Baysian statistics as a radical alternative to standard views has made the conflict especially acute. In recent years the response of many practising statisticians to the conflict has been an eclectic approach to statistical inference. Many good statisticians have developed a kind of wisdom which enables them to know which problems are most appropriately handled by each of the methods available. The search for principles which would explain why each of the methods works where it does and fails where it does offers a fruitful approach to the controversy over foundations. (shrink)

Scientific realism has been advanced as an interpretation of the natural sciences but never the behavioral sciences. This book introduces a novel version of scientific realism, Measured Realism, that characterizes the kind of theoretical progress in the social and psychological sciences that is uneven but indisputable. It proposes a theory of measurement, Population-Guided Estimation, that connects natural, psychological, and social scientific inquiry. Presenting quantitative methods in the behavioral sciences as at once successful and regulated by the world, the book (...) will engage philosophers of science, historians of science, sociologists of science, and scientists interested in the foundations of their own disciplines. Part I introduces measured realism beginning with a novel realist theory of measurement (Population-Guided Estimation, or PGE). It examines two other versions of scientific realism — minimal (Hacking) and robust (Boyd, Putnam, and Newton-Smith) — and advances measured realism as best supported by the evidence in the behavioral sciences. It also critically evaluates the anti-realist arguments of van Fraassen, Laudan, and Fine. Part II formulates and develops a version of epistemological naturalism most appropriate to measured realism. Drawing upon the realist theory of measurement developed earlier, it favorably appraises a variety of statistical methods in the behavioral sciences against selected criticisms. It concludes that traditional, narrative methods in the behavioral and social sciences are unreliable by the measures of epistemological naturalism. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific (...) reasoning. With the help of an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

My aim in this paper is to explain how universal statements, as they occur in scientific theories, are actually tested by observational evidence, and to draw certain conclusions, on that basis, about the way in which scientific theories are tested in general. 1 But I am pursuing that aim, ambitious enough in and of itself, in the service of even more ambitious projects, and in the first place: (a) to say what is distinctive about modern science, and especially modern (...) physical science, as a human intellectual activity; and (b) to show how this distinctiveness explains the unique status of modern science in human intellectual life. So I will begin by saying a few words about that larger project. One might doubt, first, whether that project is legitimate. Although everything is different from everything else, the question “What is distinctive about X?” is not necessarily well put, because X may not be anything—that is, anything distinct. And there are indeed many philosophers, otherwise of the most diverse intellectual backgrounds and tendencies, who would deny, in various ways, that modern science is a distinct thing, or that its status is unique. It seems to me that they deny something terrifyingly obvious— a fact which confronts us far more urgently than the fact that, say, ravens are black. I will put off further remarks about this until the end of the paper, however, because the discussion of my more limited present aim will focus precisely on the ways to tell when a question is well put, and whether there is such a (distinct) thing as X. That one’s methodological problems are also the subject of one’s investigation is a sign that that investigation is philosophical, although (or because) also a threat to its coherence. Second, some points about methodology. I am not a sociologist, or even a historian, nor (unlike some recent philosophers of science) will I pretend to be.. (shrink)

In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The (...) key to untangling them is the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

The Nature and Method of Economic Sciences: Evidence, Causality, and Ends argues that economic phenomena can be examined from five analytical levels: namely, a statistical descriptive approach, a causal explanatory approach, a teleological explicative approach, a normative approach and, finally, the level of application. The above viewpoints are undertaken by different but related economic sciences, including statistics and economic history, positive economics, normative economics, and the 'art of political economy'. Typically, positive economics has analysed economic phenomena using the second (...) approach, causally explaining and often trying to predict the future evolution of the economy. It has not been concerned with the ends selected by the individual or society, taking them as given. However, various new economic currents have emerged during the last 40 years, and some of these do assign a fundamental role to ends within economics. This book argues that the field of positive economics should adapt to deal with the issues that arise from this. The text attempts to discern the nature of economic phenomena, introducing the different approaches and corresponding economic sciences. It goes on to analyse the epistemological characteristics of these in the subsequent chapters, as well as their disciplinary inter-relations. This book is a valuable resource for students and scholars of the social sciences, philosophy, and the philosophy of economics. It will also be of interest to those researching political economy, and the development of economic thought. (shrink)