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This paper examines the current empirical accounting research paradigm. We ask: In general, do the estimated regressions support the promoted narratives? We focus on a regression model’s main variable of interest and consider the extent to which it contributes to the explanation of the dependent variable. We replicate 10 recently published accounting studies, all of which rely on significant t-statistics, per conventional levels, to claim rejection of the null hypothesis. Our examination shows that in eight studies, the incremental explanatory power contributed by the main variable of interest is effectively zero. For the remaining two, the incremental contribution is at best marginal. These findings highlight the apparent overreliance on t-statistics as the primary evaluation metric. A closer examination of the data shows that the t-statistics produced reject the null hypothesis primarily due to a large number of observations (N). Empirical accounting studies often require N > 10,000 to reject the null hypothesis. To avoid the drawback of t-statistics’ connection with N, we consider the implications of using Standardized Regressions (SR). The magnitude of SR coefficients indicates variables’ relevance directly. Empirical analyses establish a strong correlation between a variable’s estimated SR coefficient magnitude and its incremental explanatory power, without reference to N or t-statistics.

Biologists and philosophers of science have recently called for an extension of evolutionary theory. This so-called ‘extended evolutionary synthesis’ (EES) seeks to integrate developmental processes, extra-genetic forms of inheritance, and niche construction into evolutionary theory in a central way. While there is often agreement in evolutionary biology over the existence of these phenomena, their explanatory relevance is questioned. Advocates of EES posit that their perspective offers better explanations than those provided by ‘standard evolutionary theory’ (SET). Still, why this would be the case is unclear. Usually, such claims assume that EES’s superior explanatory status arises from the pluralist structure of EES, its different problem agenda, and a growing body of evidence for the evolutionary relevance of developmental phenomena (including developmental bias, inclusive inheritance, and niche construction). However, what is usually neglected in this debate is a discussion of what the explanatory standards of EES actually are, and how they differ from prevailing standards in SET. In other words, what is considered to be a good explanation in EES versus SET? To answer this question, we present a theoretical framework that evaluates the explanatory power of different evolutionary explanations of the same phenomena. This account is able to identify criteria for why and when evolutionary explanations of EES are better than those of SET. Such evaluations will enable evolutionary biology to find potential grounds for theoretical integration.

This paper provides resources from the philosophy of science to identify differences between explanatory norms across disciplines and to examine their impact on interdisciplinary work. While the body of literature on explanatory norms is expanding rapidly, a consensus on a theoretical framework for systematically identifying norms across disciplines has yet to be reached. The aims of this paper are twofold: (i) to provide such a framework and use it to identify and compare explanatory norms across different domains; and (ii) to derive indications about interdisciplinary practice accordingly. By pursuing these goals, this work aims to be both theoretically significant and practically relevant. It contributes to the ongoing work on explanatory norms; and offers recommendations for the analysis of interdisciplinary science.

When is it explanatorily better to adopt a conjunction of explanatory hypotheses as opposed to committing to only some of them? Although conjunctive explanations are inevitably less probable than less committed alternatives, we argue that the answer is not ‘never’. This paper provides an account of the conditions under which explanatory considerations warrant a preference for less probable, conjunctive explanations. After setting out four formal conditions that must be met by such an account, we consider the shortcomings of several approaches. We develop an account that avoids these shortcomings and then defend it by applying it to a well-known example of explanatory reasoning in contemporary science.

Many analogues to the coefficient of determination R 2 in ordinary regression models have been proposed in the context of logistic regression. Our starting point is a study of three definitions related to quadratic measures of variation. We discuss the properties of these statistics, and show that the family can be extended in a natural way by a fourth statistic with an even simpler interpretation, namely the difference between the averages of fitted values for successes and failures, respectively. We propose the name \"the coefficient of discrimination\" for this statistic, and recommend its use as a standard measure of explanatory power. In its intuitive interpretation, this quantity has no immediate relation to the classical versions of R 2 , but it turns out to be related to these by two exact relations, which imply that all these statistics are asymptotically equivalent.

How good is an explanation and when is one explanation better than another? In this paper, I address these questions by exploring probabilistic measures of explanatory power in order to defend a particular Bayesian account of explanatory goodness. Critical to this discussion is a distinction between weak and strong measures of explanatory power due to Good (Br J Philos Sci 19:123–143, 1968). In particular, I argue that if one is interested in the overall goodness of an explanation, an appropriate balance needs to be struck between the weak explanatory power and the complexity of a hypothesis. In light of this, I provide a new defence of a strong measure proposed by Good by providing new derivations of it, comparing it with other measures and exploring its connection with information, confirmation and explanatory virtues. Furthermore, Good really presented a family of strong measures, whereas I draw on a complexity criterion that favours a specific measure and hence provides a more precise way to quantify explanatory goodness.

Rigorous evaluation of generalist medical artificial intelligence (GMAI) is imperative to ensure their utility and safety before implementation in health care. Current evaluation strategies rely heavily on benchmarks, which can suffer from issues with data contamination and cannot explain how GMAI might fail (lacking explanatory power) or in what circumstances (lacking predictive power). To address these limitations, we propose a new methodology to improve the quality of GMAI evaluation using construct-oriented processes. Drawing on modern psychometric techniques, we introduce approaches to construct identification and present alternative assessment formats for different domains of professional skills, knowledge, and behaviors that are essential for safe practice. We also discuss the need for human oversight in future GMAI adoption.

Accurately identifying the dominant factor of karst ecosystem services (ESs) is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors on the spatial distribution of ESs is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used the geographical detector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored its differences in three geomorphological-type areas. Results showed that slope and vegetation coverage were the dominant factors of soil erosion, and the interactive explanatory power between the two factors was stronger. Affected by the universality of topographic relief and landscape fragmentation in the study area, the explanatory power of slope and land use type on soil erosion was optimal at low resolution. Precipitation, elevation, and land use type were the dominant factors for the spatial heterogeneity of water yield, and the interaction between precipitation and land use type explained more than 95% of water yield. The spatial variability of elevation in different geomorphological-type areas affected its optimal explanatory power, specifically, in the terrace and hill-type areas, the spatial variability of elevation was weak, its explanatory power was optimal at high resolution. While in the mountain-type areas, the spatial variability of elevation was strong, and its explanatory power was optimal at low resolution. This study quantitatively identified the optimal explanatory power of ES variables through multi-scale analysis, which aims to provide a way and basis for accurate identification of the dominant factors of karst mountain ESs and zoning optimization.

This paper provides a sorely-needed evaluation of the view that mathematical explanations in science explain by unifying. Illustrating with some novel examples, I argue that the view is misguided. For believers in mathematical explanations in science, my discussion rules out one way of spelling out how they work, bringing us one step closer to the right way. For non-believers, it contributes to a divide-and-conquer strategy for showing that there are no such explanations in science. My discussion also undermines the appeal to unifying power in support of the enhanced indispensability argument.

The Korean equity market is characterized by extreme concentration, with the top five stocks accounting for approximately 25-45 percent of total market capitalization. This study examines whether such concentration structurally distorts the Fama and French (1993) three-factor model (FF3) and assesses the impact of excluding large-cap stocks on coefficient estimates and explanatory power. I construct the original FF3 model along with three alternative versions, namely FF3_E1, FF3_E3 and FF3_E5, which sequentially exclude the top one, three and five firms by market capitalization. Using one-to-one correlations of estimated alphas and betas, the sum and difference of squared coefficients, and adjusted R2, I evaluate the robustness of the factor structure. The results show that even after excluding large caps, the FF3 model maintains highly stable coefficient estimates and slightly improved explanatory power, affirming its structural robustness. However, the downward shift in the benchmark return due to large-cap exclusion induces a systematic rise in estimated alphas. These findings confirm FF3's validity in concentrated markets while highlighting the need for careful alpha interpretation.