Falsification (Lexicon Entry)

2020

In this essay, I argue that falsificationism does not provide an adequate demarcation between science and non-science. Such inadequacy will be elaborated by examining a modus tollens argument in favour of falsificationism and the limitations of falsificationism itself.

The Encyclopedia of Clinical Psychology, 2015

Confirmation and falsification are different strategies for testing theories and characterizing the outcomes of those tests. Roughly speaking, confirmation is the act of using evidence or reason to verify or certify that a statement is true, definite, or approximately true, whereas falsification is the act of classifying a statement as false in the light of observation reports. After expounding the intellectual history behind confirmation and falsificationism, reaching back to Plato and Aristotle, I survey some of the main controversial issues and arguments that pertain to the choice between these strategies: the Raven Paradox, the Duhem/Quine problem and the Grue Paradox. Finally, I outline an evolutionary criticism of inductive Bayesian approaches based on my assumption of doxastic involuntarism.

Journal of Chemical Ecology, 1982

A disturbing feature in science is the frequent emphasis on verification of popular theories rather than on falsification of hypotheses. As Dayton and Oliver (1980) stressed recently "The verification of ideas may be the most treacherous trap in science, as counterexamples are overlooked , alternate hypotheses brushed aside, and existing paradigms manicured. The successful advance of science and the proper use of experimentation depend upon rigorous attempts to falsify hypotheses." While all disciplines of science suffer from this problem, the reliance of behavioral research on observational techniques requires that one exercise extreme caution in data interpretation. To avoid compromising the conclusions of field and laboratory studies, it is necessary to test rigorously alternative hypotheses and to rely on valid statistical techniques. In his recent review of a 1981 paper by Itagaki and Thorp, Rose (1982) concluded that the earlier paper contained "... misconceptions concerning the nature of pheromones and intraspecific communication and misinterpretations of results within the paper." From our perspective the only potentially significant criticism concerned our general approach in evaluating experimental results. The opposite approach advocated at least de facto by Rose is illustrative of the problem mentioned previously. The specific criticisms by Rose and our opposite approaches to data interpretation are discussed below. Although theoretically it takes only one case to reject a "properly framed" hypothesis, one must be sure that the results of a test are real (with regard to type I errors), exclusive of alternative hypotheses, and directly applicable to the overall question. The overall null hypothesis (H0) in our study was that long-distance chemical communication of sexual identity, agonistic state, and stress condition does not occur among adult crayfish. To falsify the overall null hypothesis, it was necessary to show that (1) statistically significant results led to rejection of H0, (2) these results were consistent with other data, and (3) the data were not equally well explained by alternative hypotheses. Two alternative hypotheses were that (1) the number of I073

Ceylon Journal of Science (Biological Sciences), 2009

Biology is generally accepted as a mainstream scientific discipline. However, philosophers of science have questioned the scientific method applied in biological sciences, specifically in evolutionary biology, ever since Karl Popper formulated his principle of falsification. Thus the only major theory in biology, Darwin's theory of evolution, was referred to by Popper as a metaphysical programme. He contended that the theory of evolution is a tautology and laws (if any) in the biological sciences should be unrestricted and universal. As biologists since then have pointed out, biology is a unique science, which requires unique methods to explain its phenomena. The principle of falsification and its application to biological sciences, the uniqueness of biology as a science necessitating different and equally valid scientific methods are discussed.

Falsificationism presents a normative theory of scientific methodology; Scientists put forward hypotheses or systems of theories and test them through experience via experimentation (Popper, 2002: 3). Falsifiability for Popper is the criterion for scientific statements to be classed as empirical, while falsification denotes the requirements necessary for a theory to be classed as falsified i.e. if we accept a statement that contradicts the statements of the theory (Popper, 2002: 66). Falsificationism thus identifies normative science and what the limits are to research, and the demarcating line between science and non-science (Ladyman, 2001: 62) (Popper, 2001a: 295). This essay will proceed as follows; (1) firstly, Popper’s Falsificationism’s strengths as a theory of scientific method will be explained and evaluated in comparison to (2) the impact of Kuhn’s theory of paradigm shifts, (3) Lakatos’ falsificationism (scientific research programmes), and (4) Feyerabends rejection of scientific method. Overall, (1) Popper’s theory falls victim to (2) Kuhn’s account, but the debate thus becomes between (3) Lakatos and the rejection of method via (4) Feyerabend, concluding with an interpretation of falsificationism as succumbing to Feyerabendian considerations

In empirical sciences, one of the best-known measures for a theory's strength is its falsifiability. This principle, originally introduced by philosopher Karl Popper (1902–1994), holds that good theories make bold and empirically testable claims that survive repeated attempts of falsification, i.e., attempts to prove that a theory is invalid. According to Popper, scientific progress requires provisional falsifi- able theories and their refutations that show where the existing theories need to be corrected. This is not, however, how majority of research in Information Systems Science appears to operate. Instead, much research follows an inductivist approach where researchers attempt to extend theories to new domains and obtain positive empirical confirmation. Such research, however, is weaker than falsifica- tion in terms of validation. We exemplify this research practice by tracing the history of IS use model development and by presenting examples of studies that suggest how falsification can be applied in IS research to examine existing theories’ boundary conditions. We summarize this essay by suggesting how falsification can be integrated fruitfully into replication and comparison studies.

In this article, an original philosophy of science describes both a symmetry and a synthesis of the methods for the verification and falsification of a theory of science. The basic concepts of this extended approach to the philosophy of science involve a distinct validation process, scientific proofs, a many-valued logic termed, U4, new terminology and basic probabilities associated with new terms that will be outlined in this article. This method can already be found applied internationally for the philosophy of science, especially in regard to physics.

Mathematical and Computational Forestry Natural Resource Sciences, 2010

The reason that Professor Zeide (Zeide, 2010) objects so strongly with Popper's falsification view of scientific theory ) is because Professor Popper and Professor Zeide are defining, interpreting and using the terms "induction", "verification" and "falsification" in different ways: they have different ontologies and metadata. There are many possible types of "induction" (logical, empirical-scientific, statistical, mathematical (which deductive, not inductive) ...etc.) but Professor Zeide seems to be defining (by usage) an "induction" I do not recognise, and seems not that of . Consider the certain demonstration that a particular swan is black (let us assume the bird is a bird and not a fish, it a swan and not a goose, or ugly duckling) by examining every feather (and assume a black swan is defined in terms of its feather colours). This is NOT "verification" in the inductive sense, as used by Popper (1968), or Hume (1748) or the ancient Greeks (e.g. Sextus Empiricus , 200), even though the word may be used in this way in colloquial English language (validate: "to prove that something is true", OED(2010)). "Inductive verification" is only meaningful in relation to the general assertion "all swans are white", which is posited as a result of induction from a number of particular cases of swans being white. Also, Popper (1968) is referring to general empirical-scientific theories when he says we can never be certain of the truth of scientific theories, the same point made about any logical induction by Hume (1748). Examples of such theories are Newton's and Einstein's theories of inertial motion and gravitation; the steady state and big-bang models of the universe; quantum mechanics; dark matter and dark energy. History confirms that we cannot be certain of the absolute truth of these grand theories. However, this does not mean we cannot be (reasonably) certain of simple empirical laws, like Boyle's law for gases, or Reineker's or the 3//2 self thinning law for unthininned forests, since these are simple descriptive relationships of empirical data under specific conditions. However, these empirical descriptions should not be claimed to be "true", since the models are only descriptive, and may not work under extreme conditions. We may note that Aristotle (350BC) believed induction could prove a "truth", but he also considered empirical evidence unnecessary for proof of truth. Many empirical descriptive relations are fitted from sample data using statistical methods, and of course we cannot be sure of absolute truth of statistical estimates. In common sense terms most people

Philosophies, 2022

Popper argued that a statistical falsification required a prior methodological decision to regard sufficiently improbable events as ruled out. That suggestion has generated a number of fruitful approaches, but also a number of apparent paradoxes and ultimately, no clear consensus. It is still commonly claimed that, since random samples are logically consistent with all the statistical hypotheses on the table, falsification simply does not apply in realistic statistical settings. We claim that the situation is considerably improved if we ask a conceptually prior question: when should a statistical hypothesis be regarded as falsifiable. To that end we propose several different notions of statistical falsifiability and prove that, whichever definition we prefer, the same hypotheses turn out to be falsifiable. That shows that statistical falsifiability enjoys a kind of conceptual robustness. These notions of statistical falsifiability are arrived at by proposing statistical analogues to intuitive properties enjoyed by exemplary falsifiable hypotheses familiar from classical philosophy of science. That demonstrates that, to a large extent, this philosophical tradition was on the right conceptual track. Finally, we demonstrate that, under weak assumptions, the statistically falsifiable hypotheses correspond precisely to the closed sets in a standard topology on probability measures. That means that standard techniques from statistics and measure theory can be used to determine exactly which hypotheses are statistically falsifiable. In other words: the proposed notion of statistical falsifiability both answers to our conceptual demands and submits to standard mathematical techniques.