Scientific Method, Misc

INTRODUCTION Philosophy of science is a study of the general nature of scientific practice, explanations, theories, and the relation of scientific knowledge ...

ENG: In this brief commentary on Sara Palermo’s article, I highlight several methodological criticisms of the data analysis and hypotheses proposed by the author. I then focus on the relevance of nocebo/placebo studies for the contemporary debate on the mind/body problem. In particular, I show how these phenomena raise questions for dualistic and neurocentric approaches that are still prevalent in philosophy. Finally, I stress the role of expectations in nocebo/placebo models, with reference to a promising theoretical framework: the predictive brain. (...) -/- ITA: In questo breve commento all’articolo di Sara Palermo mi propongo di rilevare alcune criticità relative al metodo di indagine e alla solidità dell’ipotesi suggerita dall’autrice. In seguito mi concentrerò sulla rilevanza dello studio del nocebo/placebo nell’ambito del dibattito sul rapporto mente/corpo e su come questi fenomeni mettano in discussione approcci dualisti e neurocentrici ancora pervasivi soprattutto in campo filosofico. In conclusione, mi soffermerò sul ruolo delle aspettative nella costruzione del modello del nocebo/placebo, riprendendo un contesto particolarmente promettente per l’inquadramento teorico del fenomeno: il cervello predittivo. Parole chiave: Placebo; Nocebo; Meta-analisi; Cervello bayesiano; Elaborazione predittiva. (shrink)

Dans cet ouvrage, Franck Varenne pose la question du réalisme scientifique, essentiellement dans sa forme contemporaine, et ce jusqu’aux années 1980. Il s’est donné pour cela la contrainte de focaliser l’attention sur ce que devenaient sa formulation et les réponses diverses qu’on a pu lui apporter en réaction spécifique à l’évolution parallèle qu’ont subie les notions de théories et surtout de modèles dans les sciences, à la même époque. Même si, bien sûr, on ne peut pas attribuer le considérable essor (...) des modèles au XXe siècle au projet qu’auraient eu les scientifiques de régler cette question, en grande partie philosophique, du réalisme – car les modèles scientifiques ont bien d’autres fonctions et ils proviennent de bien d’autres demandes techniques, cognitives et sociales –, son choix épistémologique a consisté à suivre la littérature contemporaine désormais classique, tant scientifique que philosophique, sur les théories puis sur les modèles afin d’une part, d’en rapporter l’évolution générale, mais, d’autre part aussi, afin de l’interroger de proche en proche, et systématiquement, sur ce qu’elle entend à chaque fois réévaluer ou remettre en débat au moyen de cette question persistante du réalisme et de la réalité en science. Au-delà de l’enquête historique, cette étude se révèle donc également comparative. Elle présente l’intérêt de mettre en évidence des similitudes de forme remarquables (identités, symétries, inversions, déplacements) entre des séquences argumentatives produites par des auteurs différents, dans des contextes distincts, au sujet de cette capacité qu’aurait – ou non – la science à rendre véritablement compte de la réalité. -/- Ainsi, via l’analyse épistémologique historique et comparative qu’en propose Franck Varenne, la question cruciale de la médiation du réel par nos outils conceptuels ou expérientiels reçoit dans ce livre l’éclairage d’auteurs dont les conceptions sont, pour certaines encore, méconnues du lecteur non anglophone : Peter Achinstein, Max Black, Ludwig Boltzmann, Nancy Cartwright, Pierre Duhem, Ian Hacking, Mary Hesse, Evelyn Fox Keller, Imre Lakatos, Ernst Mach, Ernest Nagel, Henri Poincaré, Willard V.O. Quine, Bas van Fraassen, etc. (shrink)

Simple regression (regression analysis with a single explanatory variable), and multiple regression (regression models with multiple explanatory variables), typically correspond to very different biological questions. The former use regression lines to describe univariate associations. The latter describe the partial, or direct, effects of multiple variables, conditioned on one another. We suspect that the superficial similarity of simple and multiple regression leads to confusion in their interpretation. A clear understanding of these methods is essential, as they underlie a large range of (...) procedures in common use in biology. Beyond simple and multiple regression in their most basic forms, understanding the key principles of these procedures is critical to understanding, and properly applying, many methods, such as mixed models, generalised models, and causal inference using graphs (including path analysis and its extensions). A simple, but careful, look at the distinction between these two analyses is valuable in its own right, and can also be used to clarify widely-held misconceptions about collinearity (correlations among explanatory variables). There is no general sense in which collinearity is a problem. We suspect that the perception of collinearity as a hindrance to analysis stems from misconceptions about interpretation of multiple regression models, and so we pursue discussions about these misconceptions in this light. In particular, collinearity causes multiple regression coefficients to be less precisely estimated than corresponding simple regression coefficients. This should not be interpreted as a problem, as it is perfectly natural that direct effects should be harder to characterise than univariate associations. Purported solutions to the perceived problems of collinearity are detrimental to most biological analyses. (shrink)

Book review of Laats and Siegel (2016) *Teaching Evolution in a Creation Nation* (University of Chicago Press).

Contesting the common opinion that, unlike the problem of induction, the problem of demarcation is of little significance, the paper maintains that Popper’s criterion of falsifiability gives an irresistible answer to the question of what can be learnt from an empirical investigation. Everything follows from the rejection of inductive logic, together with the recognition that, before it can be empirically investigated, a hypothesis has to be formulated and accepted. Scientific hypotheses emerge neither a posteriori, as inductivists hold, nor from some (...) immaculate a priori source, but from sheer guesswork. Empiricists who reject apriorism have therefore enlisted too zealously in the unphilosophical ranks of epistemological naturalism. The paper concludes with a summary of Popper’s objectivism, and with brief responses to some fashionable arguments that objective truth is not an attainable objective.RésuméContestant l’opinion commune selon laquelle le problème de la démarcation, contrairement au problème de l’induction, est relativement anecdotique, l’article soutient que le critère poppérien de falsifiabilité donne une réponse irrésistible à la question de savoir ce qui peut être appris d’une investigation empirique. Tout découle du rejet de la logique inductive, joint à la reconnaissance du fait que, avant d’être investiguée, une hypothèse doit être formulée et acceptée. Les hypothèses scientifiques n’émergent ni a posteriori comme les inductivistes le soutiennent, ni de quelque immaculée source a priori : elles sont des conjectures pures et simples. Les empiristes qui rejettent l’apriorisme ont donc rejoint trop rapidement les rangs non philosophiques du naturalisme épistémologique. L’article conclut par un résumé de l’objectivisme popperien et par de brèves réponses à certains arguments à la mode selon lesquels la vérité objective n’est pas un objectif atteignable. (shrink)

The central theme running throughout this outstanding new survey is the nature of the philosophical debate created by modern science's foundation in experimental and mathematical method. More recently, recognition that reasoning in science is probabilistic generated intense debate about whether and how it should be constrained so as to ensure the practical certainty of the conclusions drawn. These debates brought to light issues of a philosophical nature which form the core of many scientific controversies today. _Scientific Method: A Historical and (...) Philosophical Introduction_ presents these debates through clear and comparative discussion of key figures in the history of science. Key chapters critically discuss * Galileo's demonstrative method, Bacon's inductive method, and Newton's rules of reasoning * the rise of probabilistic `Bayesian' methods in the eighteenth century * the method of hypotheses through the work of Herschel, Mill and Whewell * the conventionalist views of Poincaré and Duhem * the inductivism of Peirce, Russell and Keynes * Popper's falsification compared with Reichenbach's enumerative induction * Carnap's scientific method as Bayesian reasoning The debates are brought up to date in the final chapters by considering the ways in which ideas about method in the physical and biological sciences have affected thinking about method in the social sciences. This debate is analyzed through the ideas of key theorists such as Kuhn, Lakatos, and Feyerabend. (shrink)

We describe a simple, flexible exercise that can be implemented in the philosophy of science classroom: students are asked to determine the contents of a closed container without opening it. This exercise has revealed itself as a useful platform from which to examine a wide range of issues in the philosophy of science and may, we suggest, even help us think about improving the public understanding of science.

Professor Schurz has written a book that is ambitious in both scope and aims. It begins with an introductory chapter on the historical development and general aims of the philosophy of science itself, moves on to issues associated with establishing a basis for a unified approach to science, with extensive consideration of the conceptual toolkit required, then takes us through chapters on laws and empirical testing, the empirical evaluation of theories more generally, including issues of realism and empiricism, before concluding (...) with an extensive and technically detailed account of causation and explanation. Chapters are divided into core and ‘complementary and advanced’ topics—so, ‘Methodological Features of Scientific Theories’ is included under the former, whereas ‘Instrumentalism and Realism—The Ontology of Scientific Theories’ is placed in the latter—and relevant case studies go beyond the usual run of examples from physics to include Piaget’s Theory of Cognitive Development .. (shrink)

What is it to be scientific? Is there such a thing as scientific method? And if so, how might such methods be justified? Robert Nola and Howard Sankey seek to provide answers to these fundamental questions in their exploration of the major recent theories of scientific method. Although for many scientists their understanding of method is something they just pick up in the course of being trained, Nola and Sankey argue that it is possible to be explicit about what this (...) tacit understanding of method is, rather than leave it as some unfathomable mystery. They robustly defend the idea that there is such a thing as scientific method and show how this might be legitimated. This book begins with the question of what methodology might mean and explores the notions of values, rules and principles, before investigating how methodologists have sought to show that our scientific methods are rational. Part 2 of this book sets out some principles of inductive method and examines its alternatives including abduction, IBE, and hypothetico-deductivism. Part 3 introduces probabilistic modes of reasoning, particularly Bayesianism in its various guises, and shows how it is able to give an account of many of the values and rules of method. Part 4 considers the ideas of philosophers who have proposed distinctive theories of method such as Popper, Lakatos, Kuhn and Feyerabend and Part 5 continues this theme by considering philosophers who have proposed naturalised theories of method such as Quine, Laudan and Rescher. This book offers readers a comprehensive introduction to the idea of scientific method and a wide-ranging discussion of how historians of science, philosophers of science and scientists have grappled with the question over the last fifty years. (shrink)

This paper explores the relationship between operations research as practised during the Second World War and the claims of many of its proponents that it constituted an application of scientific method. It begins with an examination of the pre-war work of two of the most notable leaders in wartime OR, the British experimental physicist Patrick Blackett and the American theoretical physicist Philip Morse. Despite differences in their scientific work, each saw science as an essentially creative act relying on the skill (...) and judgement of the individual scientist in the deployment of rational methods for the development of legitimate conclusions. When scientists began to study military operations, their investigations were defined by the technically sophisticated heuristic practices already surrounding military planning. They did not seek to replace these practices with their own rational methods. Rather, they became scholars of the military's methods and adapted their pre-war experience by shifting their self-disciplined attitude to their own work to bodies of military knowledge. Thus scientists learned so well to navigate an alien heuristic system that investigations they conducted within it took on the characteristics that they judged defined scientific work. (shrink)

The controversy between the medical schools of Paris and Montpellier extends roughly from the death of Barthez (1806) to the publication of the Introduction to the study of experimental medicine of Claude Bernard (1865), with a peak during which the controversy merges with the polemic between Louis Peisse and Jacques Lordat (1840-1843). This study aims to document as accurately as possible the arguments that were exchanged during this controversy, by seeking their reasons and explaining how the experimental medicine in Paris (...) ultimately took the lead over the vitalism championed by the school of medicine in Montpellier. (shrink)

Philosophy of Science: A Unified Approach combines a general introduction to philosophy of science with an integrated survey of all its important subfields. As the book’s subtitle suggests, this excellent overview is guided methodologically by "a unified approach" to philosophy of science: behind the diversity of scientific fields one can recognize a methodological unity of the sciences. This unity is worked out in this book, revealing all the while important differences between subject areas. Structurally, this comprehensive book offers a two-part (...) approach, which makes it an excellent introduction for students new to the field and a useful resource for more advanced students. Each chapter is divided into two sections. The first section assumes no foreknowledge of the subject introduced, and the second section builds upon the first by bringing into the conversation more advanced, complementary topics. Definitions, key propositions, examples and figures overview all of the core material. At the end of every chapter there are selected readings and exercises . The book also includes a comprehensive bibliography and an index. (shrink)

The question „is a science of society possible” seemingly leads into a dilemma, i. e. the choice between science without freedom or freedom without science. Positivism acknowledges this dilemma and „solves” it by opting for a science without freedom : a science of society as an object outside of and independent from individual decisions, following the example of natural science. This viewpoint is explicitly stated by Émile Durkheim in his „Rules of sociological method”. However, an analysis of the actual method (...) used by Durkheim himself in his study on suicide, demonstrates that he did not live up to these rules in his own research. What he actually did, was shown to be in complete accordance with R. Aron's thesis that Durkheim „in all his books has sought to grasp the meaning which individuals and groups assign to their ways of life”. The notion of causality - in the natural scientific sense of „conditioning” - is not useless in sociology, but always shows a missing link, i. e. the human definition of the situation, which requires its own method, the „Verstehen”. Social phenomena have a „humanistic coefficient”. Second : Durkheim pleads in his „Rules” for the exclusive rights of the inductive method. In his own research however he turns out to use a scarcely concealed sort of deductive reasoning and model-construction. So, for sociology to be a science, positivism will have to give way to another type of methodology. In fact, many sociological studies do use another method, which shows great affinity with Popper's „critical rationalism” : Pure inductivism leads into a dead end. The only possible method is the construction of hypotheses which are tested on the criterion of falsifiability. The specifying characteristic of the methods of sociology and history is reconstruction of the situational logic, in the Weberian sense of the word. This implies an interpretation in terms of subjective values. (shrink)

1. Introduction : humanity's urge to understand -- 2. Elements of scientific thinking : skepticism, careful reasoning, and exhaustive evaluation are all vital. Science Is universal -- Maintaining a critical attitude. Reasonable skepticism -- Respect for the truth -- Reasoning. Deduction -- Induction -- Paradigm shifts -- Evaluating scientific hypotheses. Ockham's razor -- Quantitative evaluation -- Verification by others -- Statistics : correlation and causation -- Statistics : the indeterminacy of the small -- Careful definition -- Science at the frontier. (...) When good theories become ugly -- Stuff that just does not fit -- 3. Christopher Columbus and the discovery of the "Indies" : it can be disastrous to stubbornly refuse to recognize that you have falsified your own hypothesis -- 4. Antoine Lavoisier and Joseph Priestley both test the befuddling phlogiston theory : junking a confusing hypothesis may be necessary to clear the way for new and productive science -- 5. Michael Faraday discovers electromagnetic induction but fails to unify electromagnetism and gravitation : it is usually productive to simplify and consolidate your hypotheses -- 6. Wilhelm Röntgen intended to study cathode rays but ended up discovering X-rays : listen carefully when Mother Nature whispers in your ear : she may be leading you to a Nobel Prize -- 7. Max Planck, the first superhero of quantum theory, saves the universe from the ultraviolet catastrophe : assemble two flawed hypotheses about a key phenomenon into a model that fits experiment exactly and people will listen to you even if you must revolutionize physics -- 8. Albert Einstein attacks the problem "Are atoms real?" from every angle : solving a centuries-old riddle in seven different ways can finally resolve it -- 9. Niels Bohr models the hydrogen atom as a quantized system with compelling exactness, but his later career proves that collaboration and developing new talent can become more significant than the groundbreaking research of any individual -- 10. Conclusions, status of science, and lessons for our time. Conclusions from our biographies -- What thought processes lead to innovation? -- Is the scientist an outsider? -- The status of the modern scientific enterprise -- Lessons for our time -- Can the scientific method be applied to public policy? -- Why so little interest in science? -- Knowledge is never complete. (shrink)

Conflict and harmony -- The disunity of science -- The abundance of nature -- Dehumanizing humans.

This is not how science works. But science does work, and here award-winning teacher and scholar Steven Gimbel provides students the tools to answer for themselves this question: What actually is the scientific method?

Bohr’s atomic model is one of the better known examples of empirically successful, albeit inconsistent, theoretical schemes in the history of physics. For this reason, many philosophers use this model to illustrate their position for the occurrence and the function of inconsistency in science. In this paper, I proceed to a critical comparison of the structure and the aims of Bohr’s research program – the starting point of which was the formulation of his model – with some of its contemporary (...) philosophical readings. My study comes to conclude that the attempt of certain philosophers to accommodate Bohr’s model to a form of paraconsistent logic obliterates essential aspects of scientists' actual practice and reasoning. (shrink)

This classic work in the philosophy of physical science is an incisive and readable account of the scientific method. Pierre Duhem was one of the great figures in French science, a devoted teacher, and a distinguished scholar of the history and philosophy of science. This book represents his most mature thought on a wide range of topics.

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. (...) In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

We present a model of the distribution of labour in science. Such models tend to rely on the mechanism of the invisible hand . Our analysis starts from the necessity of standards in distributed processes and the possibility of multiple standards in science. Invisible hand models turn out to have only limited scope because they are restricted to describing the atypical single-standard case. Our model is a generalisation of these models to J standards; single-standard models such as Kitcher are a (...) limiting case. We introduce and formalise this model, demonstrate its dynamics and conclude that the conclusions commonly derived from invisible hand models about the distribution of labour in science are not robust against changes in the number of standards. (shrink)

Over the last four decades arguments for and against the claim that creative hypothesis formation is based on Darwinian ‘blind’ variation have been put forward. This paper offers a new and systematic route through this long-lasting debate. It distinguishes between undirected, random, and unjustified variation, to prevent widespread confusions regarding the meaning of undirected variation. These misunderstandings concern Lamarckism, equiprobability, developmental constraints, and creative hypothesis formation. The paper then introduces and develops the standard critique that creative hypothesis formation is guided (...) rather than blind, integrating developments from contemporary research on creativity. On that basis, I discuss three compatibility arguments that have been used to answer the critique. These arguments do not deny guided variation but insist that an important analogy exists nonetheless. These compatibility arguments all fail, even though they do so for different reasons: trivialisation, conceptual confusion, and lack of evidence respectively. Revisiting the debate in this manner not only allows us to see where exactly a ‘Darwinian’ account of creative hypothesis formation goes wrong, but also to see that the debate is not about factual issues, but about the interpretation of these factual issues in Darwinian terms. (shrink)

The focus of this paper is to examine the problem of induction as a methodology for science. It also evaluates Karl Popper’s deductive approach as the suitable methodology for scientific research. Popper calls his theory ‘hypothetico-deductive methodology’. However, this paper argues the thesis that Popper’s theory of hypothetico-deductive methodology, which he claims is the only appropriate methodology of science is fraught with some theoretical difficulties, which makes it unacceptable. Popper’s logical asymmetry between verification and falsification, we argue, is philosophically untenable. (...) We argue the thesis for the complementarity of both inductive and deductive methodology in scientific investigation. This study seeks to establish the resonance of accepting both deductive and inductive reasoning as the basic methodologies upon which scientific research and discoveries proceed. (shrink)

A new approach to analyze scientific methods as patternsof state transitions is proposed and exemplified by the two mostimportant, general methods: induction and deduction. Though only`local' states of science are considered in this paper, includinghypotheses, data, approximation and degree of fit, the approach caneasily be extended to more comprehensive kinds of states. Two `pure'forms of induction are distinguished, enumerative and hypothesisconstruction induction. A combination of these two forms is proposedto yield a more adequate picture of induction. While the pure forms (...) ofinduction are clearly distinct from the deductive pattern, the patternof the combined form of induction is very similar to the latter. Thepresent account of scientific methods not only points out thedifferences between different methods but – in contrast to usualdiscussions of methodology – also clarifies what they have in common. (shrink)

Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data. These challenges are incomplete in two respects: they do not show that chaotic regimes are unpredictable in principle and, as a result, that there is something conceptually wrong with idealized expectations of correct predictions from acceptable theories, and they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this paper (...) we appeal to an asymptotic analysis of state space trajectories and their numerical approximations to show that chaotic regimes are deterministically unpredictable even with unbounded resources. Additionally, we explain why stochastic models of chaotic systems, while predictively successful in some cases, are in general predictively as limited as deterministic ones. We conclude by suggesting that the way in which scientists deal with such principled obstructions to predictability calls for a more comprehensive approach to theory validation, on which experimental testing is augmented by a multifaceted mathematical analysis of theoretical models, capable of identifying chaos-related predictive failures as due to principled limitations which the world itself imposes on any less-than-omniscient epistemic access to some natural systems. (shrink)

Originally published in 1961 by Columbia University Press.

Many scientists believe that there is a uniform, interdisciplinary method for the prac- tice of good science. The paradigmatic examples, however, are drawn from classical ex- perimental science. Insofar as historical hypotheses cannot be tested in controlled labo- ratory settings, historical research is sometimes said to be inferior to experimental research. Using examples from diverse historical disciplines, this paper demonstrates that such claims are misguided. First, the reputed superiority of experimental research is based upon accounts of scientific methodology (Baconian inductivism (...) or falsificationism) that are deeply flawed, both logically and as accounts of the actual practices of scientists. Second, although there are fundamental differences in methodology between experimental scien- tists and historical scientists, they are keyed to a pervasive feature of nature, a time asymmetry of causation. As a consequence, the claim that historical science is methodo- logically inferior to experimental science cannot be sustained. (shrink)