A Paradoxical Feature of the Severity Measure of Evidence

The main point of this paper is to underscore that tests with very low power will be significant only if the observations are deviant under both H0 and H1. Therefore, the results of those significant tests will generate misleadingly high severity scores for differences between H0 and H1 that are excessively overestimated. In other words, that measure of evidence is bound to fail in those cases. It will inevitably fail to adequately measure the strength of the evidence provided by tests with low power

The Principle of Total Evidence and Classical Statistical Tests

Classical statistical inferences have been criticised for various reasons. To assess the soundness of such criticisms is a very important task because they are widely used in everyday scientific research. This is one of the reasons why the philosophy of statistics is an exciting field of study. In this paper, I focus on two such criticisms. The first one claims that the use of the p-value violates (or can violate) the principle of total evidence (PTE). It is a thesis that has been defended by Elliott Sober and Bengt Autzen. The second one says that the result of classical tests does not only depend on the data but on the sampling plan of the experimenter also. The underlying criticism of course is that the sampling plan is not part of the evidence and that classical tests therefore violate PTE. The intentions of the experimenter should not affect the result of an inference. My aim is to show that both criticisms are unsound. Doing so, I hope to clarify the concept of p-value and the nature of the evidence in classical statistical tests. The point of my paper is to show that the identification of the evidence on which those criticisms rest is inadequate

Simplicity and model selection

In this paper I compare parametric and nonparametric regression models with the help of a simulated data set. Doing so, I have two main objectives. The first one is to differentiate five concepts of simplicity and assess their respective importance. The second one is to show that the scope of the existing philosophical literature on simplicity and model selection is too narrow because it does not take the nonparametric approach into account, S112–S123, 2002; Forster and Sober in The British Journal for the Philosophy of Science 45, 1–35, 1994; Forster, 2001, in Philosophy of Science 74, 588–600, 2007; Hitchcock and Sober in The British Journal for the Philosophy of Science 55, 1–34, 2004; Mikkelson in Philosophy of Science 73, 440–447, 2006; Baker 2013). More precisely, I point out that a measure of simplicity in terms of the number of adjustable parameters is inadequate to characterise nonparametric models and to compare them with parametric models. This allows me to weed out false claims about what makes a model simpler than another. Furthermore, I show that the importance of simplicity in model selection cannot be captured by the notion of parametric simplicity. ‘Simplicity’ is an umbrella term. While parametric simplicity can be ignored, there are other notions of simplicity that need to be taken into consideration when we choose a model. Such notions are not discussed in the previously mentioned literature. The latter therefore portrays an incomplete picture of why simplicity matters when we choose a model. Overall I support a pluralist view according to which we cannot give a general and interesting justification for the importance of simplicity in science

The Principle of Total Evidence and Classical Statistical Tests

Classical statistical inferences have been criticised for various reasons. To assess the soundness of such criticisms is a very important task because they are widely used in everyday scientific research. This is one of the reasons why the philosophy of statistics is an exciting field of study. In this paper, I focus on two such criticisms. The first one claims that the use of the p-value violates (or can violate) the principle of total evidence (PTE). It is a thesis that has been defended by Elliott Sober and Bengt Autzen. The second one says that the result of classical tests does not only depend on the data but on the sampling plan of the experimenter also. The underlying criticism of course is that the sampling plan is not part of the evidence and that classical tests therefore violate PTE. The intentions of the experimenter should not affect the result of an inference. My aim is to show that both criticisms are unsound. Doing so, I hope to clarify the concept of p-value and the nature of the evidence in classical statistical tests. The point of my paper is to show that the identification of the evidence on which those criticisms rest is inadequate

Statistical Power and P-values: An Epistemic Interpretation Without Power Approach Paradoxes

It has been claimed that if statistical power and p-values are both used to measure the strength of our evidence for the null-hypothesis when the results of our tests are not significant, then they can also be used to derive inconsistent epistemic judgements as we compare two different experiments. Those problematic derivations are known as power approach paradoxes. The consensus is that we can avoid them if we abandon the idea that statistical power can measure the strength of our evidence. In this paper however, I put forward a different solution. I argue that every power approach paradox rests on an equivocation on "strong evidence". The main idea is that we need to make a careful distinction between (i) the evidence provided by the quality of the test and (ii) the evidence provided by the outcome of the test. Both provide different types of evidence and their respective strength are to be evaluated differently

Constructive Empiricism and the Closure Problem

In this paper I articulate a fictionalist solution to the closure problem that affects constructive empiricism. Relying on Stephen Yablo’s recent study of closure puzzles, I show how we can partition the content of a theory in terms of its truthmakers and claim that a constructive empiricist can believe that all the observable conditions that are necessary to make a part of her theory true obtain and remain agnostic about whether or not the other truthmakers for the other parts of her theory obtain. This can be done even though she asserts her theory as if it was wholly tru

How we load our data sets with theories and why we do so purposefully

In this paper, I compare theory-laden perceptions with imputed data sets. The similarities between the two allow me to show how the phenomenon of theory-ladenness can manifest itself in statistical analyses. More importantly, elucidating the differences between them will allow me to broaden the focus of the existing literature on theory-ladenness and to introduce some much-needed nuances