Search results for '*Hypothesis Testing' (try it on Scholar)

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  1. Romeijn Jan-Willem Rens Van de Schoot, Herbert Hoijtink (2011). Moving Beyond Traditional Null Hypothesis Testing: Evaluating Expectations Directly. Frontiers in Psychology 2.score: 64.0
    This mini-review illustrates that testing the traditional null hypothesis is not always the appropriate strategy. Half in jest, we discuss Aristotle’s scientific investigations into the shape of the earth in the context of evaluating the traditional null hypothesis. We conclude that Aristotle was actually interested in evaluating informative hypotheses. In contemporary science the situation is not much different. That is, many researchers have no particular interest in the traditional null hypothesis. More can be learned from data by evaluating specific (...)
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  2. David H. Dodd & Lyle E. Bourne Jr (1969). Test of Some Assumptions of a Hypothesis-Testing Model of Concept Identification. Journal of Experimental Psychology 80 (1):69.score: 55.0
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  3. Richard T. Fink (1972). Response Latency as a Function of Hypothesis-Testing Strategies in Concept Identification. Journal of Experimental Psychology 95 (2):337.score: 55.0
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  4. Dianne S. Silver, Eli Saltz & Vito Modigliani (1970). Awareness and Hypothesis Testing in Concept and Operant Learning. Journal of Experimental Psychology 84 (2):198.score: 55.0
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  5. David P. Barash (2005). Sex Differences: Empiricism, Hypothesis Testing, and Other Virtues. Behavioral and Brain Sciences 28 (2):276-277.score: 44.0
    “Sociosexuality from Argentina to Zimbabwe: A 48-nation study of sex, culture, and strategies of human mating” delivers on its title. By combining empiricism and careful hypothesis testing, it not only contributes to our current knowledge but also points the way to further advances.
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  6. Curtis K. Deutsch, Wesley W. Ludwig & William J. McIlvane (2008). Heterogeneity and Hypothesis Testing in Neuropsychiatric Illness. Behavioral and Brain Sciences 31 (3):266-267.score: 44.0
    The confounding effects of heterogeneity in biological psychiatry and psychiatric genetics have been widely discussed in the literature. We suggest an approach in which heterogeneity may be put to use in hypothesis testing, and may find application in evaluation of the Crespi & Badcock (C&B) imprinting hypothesis. Here we consider three potential sources of etiologic subtypes for analysis.
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  7. Robert W. Frick (1998). Chow's Defense of Null-Hypothesis Testing: Too Traditional? Behavioral and Brain Sciences 21 (2):199-199.score: 44.0
    I disagree with several of Chow's traditional descriptions and justifications of null hypothesis testing: (1) accepting the null hypothesis whenever p > .05; (2) random sampling from a population; (3) the frequentist interpretation of probability; (4) having the null hypothesis generate both a probability distribution and a complement of the desired conclusion; (5) assuming that researchers must fix their sample size before performing their study.
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  8. G. William Moore, Grover M. Hutchins & Robert E. Miller (1986). A New Paradigm for Hypothesis Testing in Medicine, with Examination of the Neyman Pearson Condition. Theoretical Medicine and Bioethics 7 (3).score: 44.0
    In the past, hypothesis testing in medicine has employed the paradigm of the repeatable experiment. In statistical hypothesis testing, an unbiased sample is drawn from a larger source population, and a calculated statistic is compared to a preassigned critical region, on the assumption that the comparison could be repeated an indefinite number of times. However, repeated experiments often cannot be performed on human beings, due to ethical or economic constraints. We describe a new paradigm for hypothesis testing (...)
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  9. Bruno D. Zumbo (1998). A Viable Alternative to Null-Hypothesis Testing. Behavioral and Brain Sciences 21 (2):227-228.score: 44.0
    This commentary advocates an alternative to null-hypothesis testing that was originally represented by Rozeboom over three decades ago yet is not considered by Chow (1996). The central distinguishing feature of this approach is that it allows the scientist to conclude that the data are much better fit by those hypotheses whose values fall inside the interval than by those outside.
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  10. Dr Francesco Mancini & Amelia Gangemi (2004). The Influence of Responsibility and Guilt on Naive Hypothesis-Testing. Thinking and Reasoning 10 (3):289 – 320.score: 44.0
    Three experiments were used to investigate individuals' hypothesis-testing process as a function of moral perceived utilities , which in turn depend on perceived responsibility and fear of guilt. Moral perceived utilities are related to individuals' moral standards and specifically to people's attempt to face up to their own responsibilities, and to avoid feeling guilty of irresponsibility. The results showed that responsibility and fear of guilt in testing hypotheses involved a process defined as prudential mode , which entails focusing (...)
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  11. Fenna H. Poletiek (2009). Popper's Severity of Test as an Intuitive Probabilistic Model of Hypothesis Testing. Behavioral and Brain Sciences 32 (1):99-100.score: 44.0
    Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.
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  12. David J. Pittenger (2001). Hypothesis Testing as a Moral Choice. Ethics and Behavior 11 (2):151 – 162.score: 44.0
    Although many researchers may perceive empirical hypothesis testing using inferential statistics to be a value free process, I argue that any conclusion based on inferential statistics contains an important and intractable value judgment. Consequently, I conclude that researchers should use the same rationale for examining the ethical ramifications of committing errors in statistical inference that they use to examine the ethical parameters of a proposed research design.
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  13. Ian I. Mitroff & Tom R. Featheringham (1976). Towards a Behavioral Theory of Systemic Hypothesis-Testing and the Error of the Third Kind. Theory and Decision 7 (3):205-220.score: 44.0
    Scientific ideas neither arise nor develop in a vacuum. They are always nutured against a background of prior, partially conflicting ideas. Systemic hypothesistesting is the problem of testing scientific hypotheses relative to various systems of background knowledge. This paper shows how the problem of systemic hypothesis-testing (Sys HT) can be systematically expressed as a constrained maximimization problem. It is also shown how the error of the third kind (E III) is fundamental to the theory of Sys HT.The error (...)
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  14. Edward Erwin (1998). The Logic of Null Hypothesis Testing. Behavioral and Brain Sciences 21 (2):197-198.score: 42.0
    In this commentary, I agree with Chow's treatment of null hypothesis significance testing as a noninferential procedure. However, I dispute his reconstruction of the logic of theory corroboration. I also challenge recent criticisms of NHSTP based on power analysis and meta-analysis.
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  15. Barbara A. Spellman (1999). Hypothesis Testing: Strategy Selection for Generalising Versus Limiting Hypotheses. Thinking and Reasoning 5 (1):67 – 92.score: 42.0
    Humans appear to follow normative rules of inductive reasoning in "premise diversity tasks" that is, they know that dissimilar rather than similar evidence is better for generalising hypotheses. In three experiments, we use a "hypothesis limitation task" to compare a related inductive reasoning skill knowing how to limit hypotheses by using a negative test strategy. Participants are told that one category member has some property (e.g. Dogs have a merocrine gland) and are asked what evidence they would test to ensure (...)
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  16. Jonathan J. Koehler (1997). A Farewell to Normative Null Hypothesis Testing in Base Rate Research. Behavioral and Brain Sciences 20 (4):780-782.score: 42.0
    I agree with Gibbs that the message of the base rate literature reads differently depending on which null hypothesis is used to frame the issue. But I argue that the normative null hypothesis, H0: “People use base rates in a Bayesian manner,” is no longer appropriate. I also challenge Adler's distinction between unused and ignored base rates, and criticize Goodie's reluctance to shift research attention to the field. Macchi's arguments about textual ambiguities in traditional base rate problems suggest that empirical (...)
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  17. Henderikus J. Stam & Grant A. Pasay (1998). The Historical Case Against Null-Hypothesis Significance Testing. Behavioral and Brain Sciences 21 (2):219-220.score: 38.0
    We argue that Chow's defense of hypothesis-testing procedures attempts to restore an aura of objectivity to the core procedures, allowing these to take on the role of judgment that should be reserved for the researcher. We provide a brief overview of what we call the historical case against hypothesis testing and argue that the latter has led to a constrained and simplified conception of what passes for theory in psychology.
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  18. Jakob Hohwy, The Hypothesis Testing Brain: Some Philosophical Applications. Proceedings of the Australian Society for Cognitive Science Conference.score: 36.0
    According to one theory, the brain is a sophisticated hypothesis tester: perception is Bayesian unconscious inference where the brain actively uses predictions to <span class='Hi'>test</span>, and then refine, models about what the causes of its sensory input might be. The brain’s task is simply continually to minimise prediction error. This theory, which is getting increasingly popular, holds great explanatory promise for a number of central areas of research at the intersection of philosophy and cognitive neuroscience. I show how the theory (...)
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  19. Jan Sprenger (2014). Testing a Precise Null Hypothesis: The Case of Lindley's Paradox. Philosophy of Science 80 (5):733-744.score: 36.0
    Testing a point null hypothesis is a classical but controversial issue in statistical methodology. A prominent illustration is Lindley’s Paradox, which emerges in hypothesis tests with large sample size and exposes a salient divergence between Bayesian and frequentist inference. A close analysis of the paradox reveals that both Bayesians and frequentists fail to satisfactorily resolve it. As an alternative, I suggest Bernardo’s Bayesian Reference Criterion: (i) it targets the predictive performance of the null hypothesis in future experiments; (ii) it (...)
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  20. Stephen Rice & David Trafimow (2011). Korn and Freidlin's Misunderstanding of the Null Hypothesis Significance Testing Procedure. American Journal of Bioethics 11 (3):15-16.score: 36.0
    (2011). Korn and Freidlin's Misunderstanding of the Null Hypothesis Significance Testing Procedure. The American Journal of Bioethics: Vol. 11, No. 3, pp. 15-16.
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  21. Kristin Andrews & Brian Huss (forthcoming). Anthropomorphism, Anthropectomy, and the Null Hypothesis. Biology and Philosophy:1-19.score: 36.0
    We examine the claim that the methodology of psychology leads to a bias in animal cognition research against attributing “anthropomorphic” properties to animals (Sober in Thinking with animals: new perspectives on anthropomorphism. Columbia University Press, New York, pp 85–99, 2005; de Waal in Philos Top 27:225–280, 1999). This charge is examined in light of a debate on the role of folk psychology between primatologists who emphasize similarities between humans and other apes, and those who emphasize differences. We argue that while (...)
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  22. Adam S. Goodie (2004). Null Hypothesis Statistical Testing and the Balance Between Positive and Negative Approaches. Behavioral and Brain Sciences 27 (3):338-339.score: 36.0
    Several of Krueger & Funder's (K&F's) suggestions may promote more balanced social cognition research, but reconsidered null hypothesis statistical testing (NHST) is not one of them. Although NHST has primarily supported negative conclusions, this is simply because most conclusions have been negative. NHST can support positive, negative, and even balanced conclusions. Better NHST practices would benefit psychology, but would not alter the balance between positive and negative approaches.
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  23. George A. Morgan, Problems With Null Hypothesis Significance Testing (NHST): What Do the Textbooks Say?score: 36.0
    The first of 3 objectives in this study was to address the major problem with Null Hypothesis Significance Testing (NHST) and 2 common misconceptions related to NHST that cause confusion for students and researchers. The misconcep- tions are (a) a smaller p indicates a stronger relationship and (b) statistical signifi- cance indicates practical importance. The second objective was to determine how this problem and the misconceptions were treated in 12 recent textbooks used in edu- cation research methods and statistics (...)
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  24. Carlyle Smith & Gregory M. Rose (2000). Evaluating the Relationship Between Rem and Memory Consolidation: A Need for Scholarship and Hypothesis Testing. Behavioral and Brain Sciences 23 (6):1007-1008.score: 36.0
    The function of REM, or any other stage of sleep, can currently only be conjectured. A rational evaluation of the role of REM in memory processing requires systematic testing of hypotheses that are optimally derived from a complete synthesis of existing knowledge. Our view is that the large number of studies supporting a relationship between REM-related brain activity and memory is not easily explained away. [Vertes & Eastman].
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  25. Siu L. Chow (1998). Précis of Statistical Significance: Rationale, Validity, and Utility. Behavioral and Brain Sciences 21 (2):169-194.score: 33.0
    The null-hypothesis significance-test procedure (NHSTP) is defended in the context of the theory-corroboration experiment, as well as the following contrasts: (a) substantive hypotheses versus statistical hypotheses, (b) theory corroboration versus statistical hypothesis testing, (c) theoretical inference versus statistical decision, (d) experiments versus nonexperimental studies, and (e) theory corroboration versus treatment assessment. The null hypothesis can be true because it is the hypothesis that errors are randomly distributed in data. Moreover, the null hypothesis is never used as a categorical proposition. (...)
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  26. M. I. Charles E. Woodson (1969). Parameter Estimation Vs. Hypothesis Testing. Philosophy of Science 36 (2):203-204.score: 33.0
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  27. Jennifer Nerissa Davis (2000). A Few Tips on Hypothesis Testing. Behavioral and Brain Sciences 23 (4):600-601.score: 33.0
    Gangestad & Simpson's account of the role of good-gene sexual selection in conditional human mating strategies is reasonably convincing, but could be more so with a little more attention to (1), dropping unnecessary sub hypotheses and especially (2) the inclusion of alternative evolutionary explanations.
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  28. Jakob Hohwy (2012). Attention and Conscious Perception in the Hypothesis Testing Brain. Frontiers in Psychology 3 (96).score: 30.0
    Conscious perception and attention are difficult to study, partly because their relation to each other is not fully understood. Rather than conceiving and studying them in isolation from each other it may be useful to locate them in an independently motivated, general framework, from which a principled account of how they relate can then transpire. Accordingly, these mental phenomena are here reviewed through the prism of the increasingly influential predictive coding framework. On this framework, conscious perception can be seen as (...)
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  29. Joseph L. Austerweil & Thomas L. Griffiths (2011). Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35 (3):499-526.score: 30.0
    The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the (...)
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  30. Darrell P. Rowbottom & R. McNeill Alexander (2012). The Role of Hypotheses in Biomechanical Research. Science in Context 25 (2):247-262.score: 30.0
    This paper investigates whether there is a discrepancy between the stated and actual aims in biomechanical research, particularly with respect to hypothesis testing. We present an analysis of one hundred papers recently published in The Journal of Experimental Biology and Journal of Biomechanics, and examine the prevalence of papers which (a) have hypothesis testing as a stated aim, (b) contain hypothesis testing claims that appear to be purely presentational (i.e. which seem not to have influenced the actual (...)
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  31. George W. Watson & Robyn Berkley (2009). Testing the Value-Pragmatics Hypothesis in Unethical Compliance. Journal of Business Ethics 87 (4):463 - 476.score: 30.0
    We test conformity-related values applying the value-pragmatics hypothesis by evaluating how personal values related to compliance moderate the relationships between situational factors and unethical decisions. We examine the direct and indirect effects of the values of traditionalism, conformity, and stimulation, as they combine with the situational factors of rewards and punishments in the person–situation interaction model. We find strong support for the value-pragmatics view of ethical decision making and further build support for the person–situation interaction model.
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  32. C. J. Barnard (2011). Asking Questions in Biology: A Guide to Hypothesis Testing, Experimental Design and Presentation in Practical Work and Research Projects. Pearson.score: 30.0
  33. Gary L. Brase & James Shanteau (2011). The Unbearable Lightness of “Thinking”: Moving Beyond Simple Concepts of Thinking, Rationality, and Hypothesis Testing. Behavioral and Brain Sciences 34 (5):250-251.score: 30.0
    Three correctives can get researchers out of the trap of constructing unitary theories of : (1) Strong inference methods largely avoid problems associated with universal prescriptive normativism; (2) theories must recognize that significant modularity of cognitive processes is antithetical to general accounts of thinking; and (3) consideration of the domain-specificity of rationality render many of the present article's issues moot.
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  34. Joan H. Cantor & Charles C. Spiker (1984). Evidence for Long-Term Planning in Children's Hypothesis Testing. Bulletin of the Psychonomic Society 22 (6):493-496.score: 30.0
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  35. G. Casella & R. Berger (2001). Hypothesis Testing in Statistics. In International Encyclopedia of the Social and Behavioral Sciences. 7118--7121.score: 30.0
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  36. Lee Cronk (1991). Hypothesis Testing and Social Engineering. Behavioral and Brain Sciences 14 (2):305-306.score: 30.0
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  37. M. E. Doherty, R. D. Tweney & C. R. Mynatt (1981). Null Hypothesis Testing, Confirmation Bias and Strong Inference. In Ryan D. Tweney, Michael E. Doherty & Clifford R. Mynatt (eds.), On Scientific Thinking. Columbia University Press. 262--267.score: 30.0
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  38. Dirk Geeraerts, Caroline Gevaert & Dirk Speelman (2011). How Anger Rose: Hypothesis Testing in Diachronic Semantics. In Kathryn Allan & Justyna A. Robinson (eds.), Current Methods in Historical Semantics. De Gruyter Mouton. 73--109.score: 30.0
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  39. Chris Haufe (2013). Why Do Funding Agencies Favor Hypothesis Testing? Studies in History and Philosophy of Science Part A 44 (3):363-374.score: 30.0
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  40. Klayman Joshua & Ha Young-Won (1989). Hypothesis Testing in Rule Discovery: Strategy, Structure, and Content. Journal of Experimental Psychology 15.score: 30.0
     
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  41. Ronald T. Kellogg (1983). Age Differences in Hypothesis Testing and Frequency Processing in Concept Learning. Bulletin of the Psychonomic Society 21 (2):101-104.score: 30.0
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  42. N. Liberman (1996). Hypothesis Testing in Wason's Selection Task: Social Exchange Cheating Detection or Task Understanding. Cognition 58 (1):127-156.score: 30.0
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  43. Francesco Mancini & Amelia Gangemi (2004). The Influence of Responsibility and Guilt on Naive Hypothesis-Testing. Thinking and Reasoning 10 (3):289-320.score: 30.0
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  44. Lizbeth J. Martin (1980). Inadequate Criteria for Hypothesis Testing in Cerebral Asymmetry Research. Behavioral and Brain Sciences 3 (2):243.score: 30.0
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  45. Gerald J. Massey (2011). Quine and Duhem on Holistic Hypothesis Testing. American Philosophical Quarterly 48 (3):239-266.score: 30.0
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  46. J. David McKee (1974). The Effect of Problem Difficulty on Hypothesis Testing and an Extension of Levine's Theory. Bulletin of the Psychonomic Society 4 (3):188-190.score: 30.0
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  47. Alfred Mele (2007). Self-Deception and Hypothesis Testing. In M. Marraffa, M. De Caro & F. Ferreti (eds.), Cartographies of the Mind. Kluwer.score: 30.0
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  48. Kim Mouridsen Morten Overgaard, Jonas Lindeløv, Stinna Svejstrup, Marianne Døssing, Tanja Hvid, Oliver Kauffmann (2013). Is Conscious Stimulus Identification Dependent on Knowledge of the Perceptual Modality? Testing the “Source Misidentification Hypothesis”. Frontiers in Psychology 4.score: 30.0
    This paper reports an experiment intended to test a particular hypothesis derived from blindsight research, which we name the “source misidentification hypothesis”. According to this hypothesis, a subject may be correct about a stimulus without being correct about how she had access to this knowledge (whether the stimulus was visual, auditory, or something else). We test this hypothesis in healthy subjects, asking them to report whether a masked stimulus was presented auditorily or visually, what the stimulus was, and how clearly (...)
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  49. Kent L. Norman (1977). Hypothesis Testing in Stimulus Integration Tasks of Varying Difficulty. Bulletin of the Psychonomic Society 9 (2):106-108.score: 30.0
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  50. L. M. Osbeck (2002). Fenna H. Poletiek, Hypothesis-Testing Behaviour. International Studies in the Philosophy of Science 16 (2):187-190.score: 30.0
     
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