Do philosophers of science frequently contribute to science, and if so how? Bibliometrics helps assess how surprisingly big is the corpus of papers authored or co-authored by philosophers and published in science. Indeed, several hundreds of philosophers have published in scientific journals. It is also possible to assess how influential this work has been in terms of citations, as compared to the average number of citations in the same journals in the same year. Unsurprisingly, many of these papers authored or co-authored by philosophers and published in scientific journals are poorly cited while a handful of them are widely cited. However, the most interesting result is that there is a significant corpus of papers authored by philosophers (both published in science journals and in philosophy journals) and significantly cited in science. It is more difficult, albeit crucial, to identify the most contributive philosophical papers, namely, those which have penetrated science not only through publication or citation in science journals, but also through discussion or endorsement by some scientists. 
Based on the identification of this often neglected corpus, which we propose to call "philosophy in science" (PinS), it becomes possible to describe the most central features of this particular way of doing philosophy of science. The first feature is bibliographic: philosophers in science tend to cite little philosophy and a lot of (up-to-date) science. Second, they also address a scientific question rather than a philosophical question. Third, in doing so, they use traditional tools of philosophy of science, typically and mostly, conceptual analysis, explication of implicit claims, examination of the consistency of claims, assessment of the relevance of methods or models. More rarely, but very interestingly, they also make positive and original contributions by bridging domains of science or suggesting hypotheses. 
This different context – in particular, the specific requirements for a publication in a peer-reviewed science journal – transforms philosophy of science. Is it still philosophy? What is the difference with approaches such as "philosophy of science in practice", "complementary science", "scientific philosophy", "theory of science", and naturalism? PinS faces a double "impostor syndrome": not entirely philosophical for philosophers, and not entirely scientific for scientists. In conclusion, we will explore how PinS can respond to this double challenge. 

While statistics has a long history of passionate philosophical controversy, the last decade especially cries out for philosophical illumination. Misuses of statistics, Big Data dredging, and P-hacking make it easy to find statistically significant, but spurious, effects. This obstructs a test's ability to control the probability of erroneously inferring effects–i.e., to control error probabilities. Disagreements about statistical reforms reflect philosophical disagreements about the nature of statistical inference–including whether error probability control even matters! I describe my interventions in statistics in relation to three events. (1) In 2016 the American Statistical Association (ASA) met to craft principles for avoiding misinterpreting P-values. (2) In 2017, a "megateam" (including philosophers of science) proposed "redefining statistical significance," replacing the common threshold of P ≤ .05 with P ≤ .005. (3) In 2019, an editorial in the main ASA journal called for abandoning all P-value thresholds, and even the words "significant/significance".
A word on each. (1) Invited to be a "philosophical observer" at their meeting, I found the major issues were conceptual. P-values measure how incompatible data are from what is expected under a hypothesis that there is no genuine effect: the smaller the P-value, the more indication of incompatibility. The ASA list of familiar misinterpretations–P-values are not posterior probabilities, statistical significance is not substantive importance, no evidence against a hypothesis need not be evidence for it–I argue, should not be the basis for replacing tests with methods less able to assess and control erroneous interpretations of data. (Mayo 2016, 2019). (2) The "redefine statistical significance" movement appraises P-values from the perspective of a very different quantity: a comparative Bayes Factor. Failing to recognize how contrasting approaches measure different things, disputants often talk past each other (Mayo 2018). (3) To ban P-value thresholds, even to distinguish terrible from warranted evidence, I say, is a mistake (2019). It will not eradicate P-hacking, but it will make it harder to hold P-hackers accountable. A 2020 ASA Task Force on significance testing has just been announced. (I would like to think my blog errorstatistics.com helped.)
To enter the fray between rival statistical approaches, it helps to have a principle applicable to all accounts. There's poor evidence for a claim if little if anything has been done to find it flawed even if it is. This forms a basic requirement for evidence I call the severity requirement. A claim passes with severity only if it is subjected to and passes a test that probably would have found it flawed, if it were. It stems from Popper, though he never adequately cashed it out. A variant is the frequentist principle of evidence developed with Sir David Cox (Mayo and Cox 2006). How did I publish with so eminent a statistician? I invited him to a session I ran on philosophy of statistics at a statistics conference. The upshot of our work is to reconcile Fisherian and Neyman-Pearson approaches, denying they form the "inconsistent hybrid" many aver. 

When scientists disagree with each other, and when their disagreement seems to not be decidable by observation or experiment, this may indicate that the disagreement is philosophical. Such situations provide an opening for philosophers to intervene in science in a way that scientists will recognize as relevant to their own work. I will describe two examples (one from evolutionary biology, the other from cognitive science) in which scientists have disagreed about the import of parsimony considerations for theory evaluation. In evolutionary biology, cladistic parsimony is a well-defined method for inferring genealogical relationships among species based on their observed similarities and differences. Cladists have defended this method by appealing to Popperian ideas about falsifiability and also by arguing that cladistic parsimony is a measure of a genealogical hypothesis's explanatory power (Farris 1983). Biologists critical of cladistic parsimony have argued that cladistic parsimony, unlike maximum likelihood, is statistically inconsistent (Felsenstein 1978). In cognitive science, psychologists have carried out several ingenious experiments to gauge whether chimpanzees are "mind-readers" (meaning that chimpanzees form beliefs about the mental states of others) or are merely "behavior-readers" (meaning that chimpanzees aren't mind-readers, but merely form beliefs about the behaviors of others). Friends of the behavior-reading hypothesis have defended it on grounds of parsimony (Povinelli and Vonk 2004), but friends of mind-reading have argued that the mind-reading hypothesis is more parsimonious because it provides unifying explanations of diverse observations, whereas no behavior-reading hypothesis can unify those observations (Tomasello and Call 2006). In both instances, debates about parsimony matter to the practice of science, and the debates have substantive philosophical content.


References


Farris, J. S. (1983) "The Logical Basis of Phylogenetic Analysis." In N. Platnick and V. Funk (eds.), Advances in Cladistics – Proceedings of the 2nd Annual Meeting of the Willi Hennig Society. New York, NY: Columbia University Press, pp. 7-36. Reprinted in E. Sober (ed.), Conceptual Issues in Evolutionary Biology, Cambridge: MIT Press, 1994, pp. 333-362.


Felsenstein J. (1978) "Cases in which Parsimony and Compatibility Methods can be Positively Misleading."Sytematic Biology27: 401-410.


Povinelli, D. and Vonk, J. (2004) "We Don't Need a Microscope to Explore the Chimpanzee's Mind." Mind and Language 19:1-28.


Tomasello, M. & Call, J. (2006) "Do Chimpanzees Know what Others See, or Only What They are Looking at?" In S. Hurley and M. Nudds (eds.), Rational Animals?. New York, NY: Oxford University Press, pp. 371-384.

The extensive literature on the definition of disease shows little sign of reaching a consensus. Several authors have argued that progress will come from focusing on what disease is, and clearly distinguishing this from the question of what 'disease' means (Lemoine 2013; Griffiths and Matthewson 2018). In the meantime, new evolutionary explanations of why natural selection has left bodies vulnerable to disease have deep and often counter-intuitive implications for understanding what disease is (Nesse, 2001, Nesse 2005b; Nesse et al. 2010). In this presentation we use theories of conceptual change in science(Stotz and Griffiths 2008) to show how specific proposals about the evolution of disease vulnerability can help explain the difficulty in defining health and disease and suggest a path forward. 
A central insight of evolutionary medicine is that that selection has shaped organisms not for health and longevity, but instead to maximize the representation of genes in future populations. We provide examples of evolved traits and mechanisms that compromise health and welfare and address the question of whether the resulting problems should be considered diseases. We also address the question of how normal but damaging defences such as inflammation are related to the concept of disease, and the related question of the status of pain and painful emotions that are often aroused normally when they are not necessary in the individual instance because they are inexpensive compared to the large cost of failing to respond if a threat is actually present (Nesse 2005a) Implications also follow from the insight that whether a phenotype is healthy or diseased depends on the specific environment for which a species was prepared to function and from recognition that an organism is a bundle of trade-offs, so that medical norms for a single phenotype need to take account other phenotypes of the same individual, both simultaneously and at other points in the life cycle.
These insights from evolutionary biology for understanding what disease is have major implications for how we conceptualize and define health and disease. They suggest an approach that differs in fundamental ways from traditional medical perspectives that are based on pre-Darwinian 'ideal-type' thinking and the demands of patients for intervention.
Moreover, disease is obviously not a concept restricted to technical, scientific contexts. At least in application to humans, these concepts are used mainly in the context of the clinic and in efforts to relief individual suffering. We draw on existing philosophical work on such processes in our attempt to elucidate the interplay of scientific, pragmatic and normative drivers of conceptual change in the case of disease (Hacking 1995; Griffiths 2004).
Core References
Griffiths, Paul E., and John Matthewson. 2018. "Evolution, Dysfunction, and Disease: A Reappraisal." The British Journal for the Philosophy of Science 69 (2): 301–27.
Nesse, Randolph M. 2001. "On the Difficulty of Defining Disease: A Darwinian Perspective." Medical Health Care and Philosophy 4 (1): 37–46.