Several researchers have recently argued that p values lose their meaning in exploratory analyses due to an unknown inflation of the alpha level (e.g., Nosek & Lakens, 2014; Wagenmakers, 2016). For this argument to be tenable, the familywise error rate must be defined in relation to the number of hypotheses that are tested in the same study or article. Under this conceptualization, the familywise error rate is usually unknowable in exploratory analyses because it is (...) usually unclear how many hypotheses have been tested on a spontaneous basis and then omitted from the final research report. In the present article, I argue that it is inappropriate to conceptualize the familywise error rate in relation to the number of hypotheses that are tested. Instead, it is more appropriate to conceptualize familywise error in relation to the number of different tests that are conducted on the same null hypothesis in the same study. Under this conceptualization, alpha level adjustments in exploratory analyses are (a) less necessary and (b) objectively verifiable. As a result, p values do not lose their meaning in exploratory analyses. (shrink)

During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., “H1,1 or H1,2”). However, in some cases, they do not make this inference. Instead, they make separate inferences about each of the individual hypotheses that comprise the joint hypothesis (e.g., H1,1 and H1,2). For example, a researcher might use a Bonferroni correction to adjust their alpha level from the conventional level of (...) 0.050 to 0.025 when testing H1,1 and H1,2, find a significant result for H1,1 (p < 0.025) and not for H1,2 (p > .0.025), and so claim support for H1,1 and not for H1,2. However, these separate individual inferences do not require an alpha adjustment. Only a statistical inference about the union alternative hypothesis “H1,1 or H1,2” requires an alpha adjustment because it is based on “at least one” significant result among the two tests, and so it refers to the familywise error rate. Hence, an inconsistent correction occurs when a researcher corrects their alpha level during multiple testing but does not make an inference about a union alternative hypothesis. In the present article, I discuss this inconsistent correction problem, including its reduction in statistical power for tests of individual hypotheses and its potential causes vis-à-vis error rate confusions and the alpha adjustment ritual. I also provide three illustrations of inconsistent corrections from recent psychology studies. I conclude that inconsistent corrections represent a symptom of statisticism, and I call for a more nuanced inference-based approach to multiple testing corrections. (shrink)

Scientists often adjust their significance threshold during null hypothesis significance testing in order to take into account multiple testing and multiple comparisons. This alpha adjustment has become particularly relevant in the context of the replication crisis in science. The present article considers the conditions in which this alpha adjustment is appropriate and the conditions in which it is inappropriate. A distinction is drawn between three types of multiple testing: disjunction testing, conjunction testing, and individual testing. It is argued that alpha (...) adjustment is only appropriate in the case of disjunction testing, in which at least one test result must be significant in order to reject the associated joint null hypothesis. Alpha adjustment is inappropriate in the case of conjunction testing, in which all relevant results must be significant in order to reject the joint null hypothesis. Alpha adjustment is also inappropriate in the case of individual testing, in which each individual result must be significant in order to reject each associated individual null hypothesis. The conditions under which each of these three types of multiple testing is warranted are examined. It is concluded that researchers should not automatically assume that alpha adjustment is necessary during multiple testing. Illustrations are provided in relation to joint studywise hypotheses and joint multiway ANOVAwise hypotheses. (shrink)

Three new versions of Wason’s 2-4-6 rule discovery task incorporating error rates or feedback of uncertainty reduction, inspired by the error-statistical account in philosophy of science, were employed. In experiments 1 and 2, participants were instructed that some experimenter feedback would be erroneous (control was original 2-4-6 without error). The results showed that performance was impaired when there was probabilistic error. In experiment 3, participants were given uncertainty reduction feedback as they generated different number triples and (...) the negative effects of probabilistic error were not observed. These findings are informative not only about rule discovery tasks in general but also about contexts of inference under uncertainty. (shrink)

Three new versions of Wason’s 2-4-6 rule discovery task incorporating error rates or feedback of uncertainty reduction, inspired by the error-statistical account in philosophy of science, were employed. In experiments 1 and 2, participants were instructed that some experimenter feedback would be erroneous. The results showed that performance was impaired when there was probabilistic error. In experiment 3, participants were given uncertainty reduction feedback as they generated different number triples and the negative effects of probabilistic error (...) were not observed. These findings are informative not only about rule discovery tasks in general but also about contexts of inference under uncertainty. (shrink)

Software-intensive systems are ubiquitous in the industrialized world. The reliability of software has implications for how we understand scientific knowledge produced using software-intensive systems and for our understanding of the ethical and political status of technology. The reliability of a software system is largely determined by the distribution of errors and by the consequences of those errors in the usage of that system. We select a taxonomy of software error types from the literature on empirically observed software errors and (...) compare that taxonomy to Giuseppe Primiero’s Minds and Machines 24: 249–273, (2014) taxonomy of error in information systems. Because Primiero’s taxonomy is articulated in terms of a coherent, explicit model of computation and is more fine-grained than the empirical taxonomy we select, we might expect Primiero’s taxonomy to provide insights into how to reduce the frequency of software error better than the empirical taxonomy. Whether using one software error taxonomy can help to reduce the frequency of software errors better than another taxonomy is ultimately an empirical question. (shrink)

The inflation of Type I error rates is thought to be one of the causes of the replication crisis. Questionable research practices such as p-hacking are thought to inflate Type I error rates above their nominal level, leading to unexpectedly high levels of false positives in the literature and, consequently, unexpectedly low replication rates. In this article, I offer an alternative view. I argue that questionable and other research practices do not usually inflate relevant Type I error (...) rates. I begin with an introduction to Type I error rates that distinguishes them from theoretical errors. I then illustrate my argument with respect to model misspecification, multiple testing, selective inference, forking paths, exploratory analyses, p-hacking, optional stopping, double dipping, and HARKing. In each case, I demonstrate that relevant Type I error rates are not usually inflated above their nominal level, and in the rare cases that they are, the inflation is easily identified and resolved. I conclude that the replication crisis may be explained, at least in part, by researchers’ misinterpretation of statistical errors and their underestimation of theoretical errors. (shrink)

We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide a (...) common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held-out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions. (shrink)

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of (...) social neuroscience that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)

Many factors are thought to increase the chances of misrecognizing a word in ASR, including low frequency, nearby disfluencies, short duration, and being at the start of a turn. However, few of these factors have been formally examined. This paper analyzes a variety of lexical, prosodic, and disfluency factors to determine which are likely to increase ASR error rates. Findings include the following. (1) For disfluencies, effects depend on the type of disfluency: errors increase by up to 15% (absolute) (...) for words near fragments, but decrease by up to 7.2% (absolute) for words near repetitions. This decrease seems to be due to longer word duration. (2) For prosodic features, there are more errors for words with extreme values than words with typical values. (3) Although our results are based on output from a system with speaker adaptation, speaker differences are a major factor influencing error rates, and the effects of features such as frequency, pitch, and intensity may vary between speakers. (shrink)

method. It is shown that the stochastic method obtains test set gains of +0.98 BLEU on MT03.

The ability to explain the occurrence of errors in children's speech is an essential component of successful theories of language acquisition. The present study tested some generativist and constructivist predictions about error on the questions produced by ten English-learning children between 2 and 5 years of age. The analyses demonstrated that, as predicted by some generativist theories [e.g. Santelmann, L., Berk, S., Austin, J., Somashekar, S. & Lust. B. (2002). Continuity and development in the acquisition of inversion in yes/no (...) questions: dissociating movement and inflection, Journal of Child Language, 29, 813-842], questions with auxiliary DO attracted higher error rates than those with modal auxiliaries. However, in wh-questions, questions with modals and DO attracted equally high error rates, and these findings could not be explained in terms of problems forming questions with why or negated auxiliaries. It was concluded that the data might be better explained in terms of a constructivist account that suggests that entrenched item-based constructions may be protected from error in children's speech, and that errors occur when children resort to other operations to produce questions [e.g. Dabrowska, E. (2000). From formula to schema: the acquisition of English questions. Cognitive Liguistics, 11, 83-102; Rowland, C. F. & Pine, J. M. (2000). Subject-auxiliary inversion errors and wh-question acquisition: What children do know? Journal of Child Language, 27, 157-181; Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge, MA: Harvard University Press]. However, further work on constructivist theory development is required to allow researchers to make predictions about the nature of these operations. (shrink)

Many contemporary philosophers rate error theories poorly. We identify the arguments these philosophers invoke, and expose their deficiencies. We thereby show that the prospects for error theory have been systematically underestimated. By undermining general arguments against all error theories, we leave it open whether any more particular arguments against particular error theories are more successful. The merits of error theories need to be settled on a case-by-case basis: there is no good general argument against (...) error theories. (shrink)

The paper offers a partial vindication of Sterelny’s view on the role of error rates and reliability in his theory of decoupled representation based on modelling techniques borrowed from the biological literature on evolution in stochastic environments. In the case of a tight link between tracking states and behaviour, I argue that in its full generality Sterelny’s account instantiates the base-rate fallacy. With regard to non-tightly linked behaviour, I show that Sterelny’s account can be vindicated subject to an (...) adequate evolutionary model and a suitable notion of reliability. (shrink)

Introduction Errors are inevitable in medical practice and this issue has attracted the attention of healthcare systems worldwide. Therefore, it is of utmost importance to pay attention in educational systems. The present study aimed to investigate the frequency and cause of nursing students’ errors. Methods This descriptive study conducted based on a cross-sectional design. The researcher provided nursing students with a questionnaire. The participants were selected through a purposive sampling method. Eventually, the collected data were analyzed by SPSS17. Results The (...) mean value of errors in students was 32.83 ± 28.5 and the prevalence rate of the errors ranged from 0 to 175. In total, 2922 errors were reported by the students, the most frequent of which were wearing the same pair of gloves for the care of more than one patient, negligence to wash the hands and clean the thermometer before utilization. Moreover, errors in oxygen therapy and medication obtained the lowest frequency. Furthermore, most of the students’ errors were due to not having enough knowledge about medicines, lack of appropriate equipment, and poor clinical skills. Discussion and conclusion: The most frequent nursing students’ errors are related to hand hygiene and personal protection. In addition, the lack of enough skills and appropriate equipment are prevalent causes of nursing students’ errors. Authorities in this field should attend to these errors in nursing education, clinical practice, and nursing studies in order to promote this profession in all of these three dimensions. (shrink)

Much evidence suggests that, from a young age, humans are able to generalize information learned about a subset of a category to the category itself. Here, we propose that—beyond simply being able to perform such generalizations—people are biased to generalize to categories, such that they routinely make spontaneous, implicit category generalizations from information that licenses such generalizations. To demonstrate the existence of this bias, we asked participants to perform a task in which category generalizations would distract from the main goal (...) of the task, leading to a characteristic pattern of errors. Specifically, participants were asked to memorize two types of novel facts: quantified facts about sets of kind members and generic facts about entire kinds. Moreover, half of the facts concerned properties that are typically generalizable to an animal kind, and half concerned properties that are typically more idiosyncratic. We predicted that—because of the hypothesized bias—participants would spontaneously generalize the quantified facts to the corresponding kinds, and would do so more frequently for the facts about generalizable properties. In turn, these generalizations would lead to a higher rate of quantified-to-generic memory errors for the generalizable properties. The results of four experiments supported this prediction. Moreover, the same generalizable-versus-idiosyncratic difference in memory errors occurred even under cognitive load, which suggests that the hypothesized bias operates unnoticed in the background, requiring few cognitive resources. In sum, this evidence suggests the presence of a powerful bias to draw generalizations about kinds. (shrink)

The Cognitive Reflection Test (CRT) is a measure of analytical reasoning that cues an intuitive but incorrect response that must be rejected for successful performance to be attained. The CRT yields two types of errors: Intuitive errors, which are attributed to Type 1 processes; and non-intuitive errors, which result from poor numeracy skills or deficient reasoning. Past research shows that participants who commit the highest numbers of errors on the CRT overestimate their performance the most, whereas those with the lowest (...) error-rates tend to slightly underestimate. This is an example of the Dunning-Kruger Effect (DKE). The present study examined how intuitive vs. non-intuitive errors contribute to overestimation in the CRT at different levels of performance. Female undergraduate students completed a seven-item CRT test and subsequently estimated their raw score. They also filled out the Faith in Intuition (FI) questionnaire, which is a dispositional measure of intuitive thinking. Data was separated into quartiles based on level of performance on the CRT. The results demonstrated the DKE. Additionally, intuitive and non-intuitive errors predicted miscalibration among low, but not high performers. However, intuitive errors were a stronger predictor of miscalibration. Finally, FI was positively correlated with CRT self-estimates and miscalibration, indicating that participants who perceived themselves to be more intuitive were worse at estimating their score. These results taken together suggest that participants who perform poorly in the CRT and also those who score higher in intuitive thinking disposition are more susceptible to the influences of heuristic-based cues, such as answer fluency, when judging their performance. (shrink)

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively (...) larger role in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)

The replication (replicability, reproducibility) crisis in social psychology and clinical medicine arises from the fact that many apparently well-confirmed experimental results are subsequently overturned by studies that aim to replicate the original study. The culprit is widely held to be poor science: questionable research practices, failure to publish negative results, bad incentives, and even fraud. In this article I argue that the high rate of failed replications is consistent with high-quality science. We would expect this outcome if the field (...) of science in question produces a high proportion of false hypotheses prior to testing. If most of the hypotheses under test are false, then there will be many false hypotheses that are apparently supported by the outcomes of well conducted experiments and null hypothesis significance tests with a type-I error rate (α) of 5%. Failure to recognize this is to commit the fallacy of ignoring the base rate. I argue that this is a plausible diagnosis of the replication crisis and examine what lessons we thereby learn for the future conduct of science. (shrink)

The replication crisis has caused researchers to distinguish between exact replications, which duplicate all aspects of a study that could potentially affect the results, and direct replications, which duplicate only those aspects of the study that are thought to be theoretically essential to reproduce the original effect. The replication crisis has also prompted researchers to think more carefully about the possibility of making Type I errors when rejecting null hypotheses. In this context, the present article considers the utility of two (...) types of Type I error probability: the Neyman–Pearson long run Type I error rate and the Fisherian sample-specific Type I error probability. It is argued that the Neyman–Pearson Type I error rate is inapplicable in social science because it refers to a long run of exact replications, and social science deals with irreversible units that make exact replications impossible. Instead, the Fisherian sample-specific Type I error probability is recommended as a more meaningful way to conceptualize false positive results in social science because it can be applied to each sample-specific decision about rejecting the same substantive null hypothesis in a series of direct replications. It is concluded that the replication crisis may be partly due to researchers’ unrealistic expectations about replicability based on their consideration of the Neyman–Pearson Type I error rate across a long run of exact replications. (shrink)

The issue of death due to medical errors is not new. We have all heard horror stories about patients dying in the hospital because of a drug mix-up or a surgery patient having the wrong limb amputated. Most people believed these stories were the exception to the rule until November 1999, when the Institute of Medicine issued a report entitled To Err Is Human: Building A Safer Health System. This report focused on medical errors and patient safety in U.S. hospitals. (...) The report indicated that as many as 44,000 to 98,000 people die each year in hospitals as a result of medical errors. These numbers suggest that more Americans are killed in U.S.hospitals every 6 months than died in the entire Vietnam War, and some have compared the alleged rate to fully loaded jumbo jets crashing every other day. This report was not without its critics. (shrink)

The issue of death due to medical errors is not new. We have all heard horror stories about patients dying in the hospital because of a drug mix-up or a surgery patient having the wrong limb amputated. Most people believed these stories were the exception to the rule until November 1999, when the Institute of Medicine issued a report entitled To Err Is Human: Building A Safer Health System. This report focused on medical errors and patient safety in U.S. hospitals. (...) The report indicated that as many as 44,000 to 98,000 people die each year in hospitals as a result of medical errors. These numbers suggest that more Americans are killed in U.S.hospitals every 6 months than died in the entire Vietnam War, and some have compared the alleged rate to fully loaded jumbo jets crashing every other day. This report was not without its critics. (shrink)

Recent work has demonstrated that academic research faces serious challenges. Incentives to defend publishable ideas often lead researchers astray. Despite their tendency to produce error, efforts to publish erroneous results typically help a researcher's career. In addition, errors often arise from seemingly innocent methodological assumptions that allow researchers to believe their research is sound. This article discusses this research, as well as research into difficulties facing epistemic rationality caused by nonepistemic incentives. It then applies the lessons of this research (...) to philosophical practice. It explains why philosophy likely suffers from these problems. It then provides an example of a widely shared methodological assumption that allows such research to be pursued and regularly published. It claims that the significance philosophers place on arguments is inappropriate, and that typical evaluations of philosophical arguments involve an instance of the base-rate fallacy. It concludes by discussing whether or not this article is self-defeating. (shrink)

According to the variety of evidence thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than, for example, replications of analogous studies. This thesis is known to fail (Bovens and Hartmann; Claveau). However, the results obtained by Bovens and Hartmann only concern instruments whose evidence is either fully random or perfectly reliable; instead, for Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model that (...) formalizes both reliability and unreliability differently. Our instruments either are reliable, but affected by random error, or are biased but not deterministically so.Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of ‘too-good-to-be-true’ evidence. This happens precisely because they contemplate neither the role of systematic bias, nor unavoidable random error of reliable instruments. In our model, the variety of evidence thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann and Claveau, and holds for (the majority of) realistic cases (that is, where biased instruments are very biased). The essential mechanism that triggers variety of evidence thesis failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

Based on the heterogeneous multiscale method, this paper presents a finite volume method to solve multiscale convection-diffusion-reaction problem. The paper constructs an algorithm of the optimal order convergence rate in H1-norm under periodic medias.

In this article we investigate origins of several cases of failure of Artificial Intelligence (AI) systems employing machine learning and deep learning. We focus on omission and commission errors in (a) the inputs to the AI system, (b) the processing logic, and (c) the outputs from the AI system. Our framework yields a set of 28 factors that can be used for reconstructing the path of AI failures and for determining corrective action. Our research helps identify emerging themes of inquiry (...) necessary for developing more robust AI-ML systems. We are hopeful that our work will help strengthen the use of machine-learning AI by enhancing the rates of true positive and true negative judgements from AI systems, and by lowering the probabilities of false positive and false negative judgements. (shrink)

Research on dyscalculia in neurodegenerative diseases is still scarce, despite high impact on patients’ independence and activities of daily living function. Most studies address Alzheimer’s Disease; however, patients with Parkinson’s Disease also have a higher risk for cognitive impairment while the relation to arithmetic deficits in financial contexts has rarely been studied. Therefore, the current exploratory study investigates deficits in two simple arithmetic tasks in financial contexts administered within the Clinical Dementia Rating in a sample of 100 PD patients. Patients (...) were classified as cognitively normal or mildly impaired according to Level I consensus criteria, and assessed using a comprehensive neuropsychological test battery, neurological motor examination, and sociodemographic and clinical questionnaires. In total, 18% showed arithmetic deficits: they were predominately female, had longer disease duration, more impaired global cognition, but minor signs of depression compared to PD patients without arithmetic deficits. When correcting for clinical and sociodemographic confounders, greater impairments in attention and visuo-spatial/constructional domains predicted occurrence of arithmetic deficits. The type of deficit did not seem to be arbitrary but seemed to involve impaired place × value processing frequently. Our results argue for the importance of further systematic investigations of arithmetic deficits in PD with sensitive tests to confirm the results of our exploratory study that a specific subgroup of PD patients present themselves with dyscalculia. (shrink)

Research in experimental epistemology has revealed a great, yet unsolved mystery: why do ordinary evaluations of knowledge ascribing sentences involving stakes and error appear to diverge so systematically from the predictions professional epistemologists make about them? Two recent solutions to this mystery by Keith DeRose (2011) and N. Ángel Pinillos (2012) argue that these differences arise due to specific problems with the designs of past experimental studies. This paper presents two new experiments to directly test these responses. Results vindicate (...) previous findings by suggesting that (i) the solution to the mystery is not likely to be based on the empirical features these theorists identify, and (ii) that the salience of ascriber error continues to make the difference in folk ratings of third-person knowledge ascribing sentences. (shrink)

Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to (...) fat tails, regardless of the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails). (shrink)

Error statistics is an important methodological view in philosophy of statistics and philosophy of science that can be applied to scientific experiments such as clinical trials. In this paper, I raise two potential issues for ES when it comes to guiding, and explaining early stopping of randomized controlled trials : ES provides limited guidance in cases of early unfavorable trends due to the possibility of trend reversal; ES is silent on how to prospectively control error rates in experiments (...) requiring multiple interim analyses. The method of conditional power, together with a rationing principle for RCTs, can assist ES in addressing such issues. (shrink)

According to the Variety of Evidence Thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than e.g. replications of analogous studies. This thesis is known to fail Bovens and Hartmann, Claveau. How- ever, the results obtained by the former only concern instruments whose evidence is either fully random or perfectly reliable; instead in Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model which formalises (...) both reliability, and unreliability, differently. Our instruments are either reliable, but affected by random error, or they are biased but not deterministically so. Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of “too-good-to- be-true” evidence. This happens precisely because they neither contemplate the role of systematic bias, nor unavoidable random error of reliable instruments. In our model the Variety of Evidence Thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann, Claveau and holds for realistic cases. The essential mechanism which triggers VET failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit :127–138, 2010. doi: 10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological (...) states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minimisation? This problem has been taken up in recent research concerned with epistemic action—actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world playfully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error :e1003094, 2013. doi: 10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi: 10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action readiness. We then go on to apply this ecological-enactive account of error dynamics to provide an answer to the first question we have raised: It may explain why it should feel good to an agent to be curious and playful. Our ecological-enactive account also allows us to show how error dynamics may provide an answer to the second question we have raised regarding how precision-weighting works in active inference. An agent that is sensitive to rates of error reduction can tune precision on the fly. We show how this ability to tune precision on the go can allow agents to develop skills for adapting better and better to the unexpected, and search out opportunities for resolving uncertainty and progressing in its learning. (shrink)

Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse (...) the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. The method can avoid preprocessing operations such as target localization and segmentation; then, we use MET features and combine with SVM to test the human body error database and compare the experimental results obtained by using different feature reduction and classification methods, and the results show that the method has the obvious comparative advantage in the recognition rate and is suitable for other dynamic scenes. (shrink)

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart (...) class='Hi'>rate risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care. Recurrent neural network, long short-term memory, gated recurrent units, and bidirectional long short-term memory are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance. (shrink)

This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute (...) deductive calculations based on known probabilities among events. As a result, the ampliative dimension of frequentist induction—learning from data about the underlying data-generating mechanism—is missing. *Received August 2009; revised January 2010. †To contact the author, please write to: Department of Economics, Virginia Tech, Blacksburg, VA 24061; e-mail: [email protected]. (shrink)

Barbey & Sloman's (B&S's) base-rate respect model is anticipated by Reyna's denominator neglect model. There are parallels at three levels: (a) explanations are grounded in a general cognitive theory (rather than in domain-specific ideas); (b) problem structure is treated as a key source of reasoning errors; and most importantly, (c) nested set relations are seen as the cause of base-rate neglect.

Using both quantitative and qualitative approaches, this study investigated the preference of learners of English as a foreign language for four types of written corrective feedback, which are often discussed in the literature, on grammatical, lexical, orthographic, and pragmatic errors. In particular, it concerned whether such preference is influenced by two learner variables, namely, foreign language enjoyment and proficiency level. The preference for selective vs. comprehensive WCF was also examined. The participants in the study were 117 University students in a (...) Thai EFL context. Analysis of questionnaire data revealed a tendency for learners to prefer more explicit types of WCF for most error types, irrespective of their proficiency and FLE level. High proficiency level learners rated less explicit WCF types as useful to some degree, whereas their low proficiency level counterparts did not. Similar results were found for the two FLE groups. Besides, the FLE level seemed to play a role in perceiving the value of WCF in terms of scope. The results of follow-up interviews showed that the linguistic features of learners' first language, existing knowledge of the target language, affective feelings, and teacher's role were the main factors contributing to variation in learners' preferences. Possible pedagogical implications are discussed. (shrink)

The installation error of metallurgical machinery parts is one of the common sources of errors in mechanical equipment. Because the installation error of different parts has different influences on different mechanical equipment, a simple linear formula cannot be used to identify the installation error. In the past, the manual recognition method and the touch recognition method lack of error information analysis, which leads to inaccurate recognition results. To improve the problem, an automatic recognition method based on (...) the neural network for metallurgical machinery parts installation error is proposed. According to the principle of automatic recognition of installation error based on the neural network, the nonlinear relation matrix between layers of the neural network is established. The operating state parameters of mechanical equipment are calculated, and the time series of the parameters are divided into several segments averagely. Based on the recognition algorithm, the inspection steps of depth, perpendicularity and center position of reserved hole, base board construction, short-circuit motor line and terminal installation, center mark board, and reference point installation are designed. The experimental results show that the recall rate of the proposed method is 97.66%, and the maximum absolute deviation is 0.09. The experimental data verify the feasibility of the proposed method. (shrink)

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient technique for estimating river discharges for steady as well as unsteady flows. It (...) also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm, the modified honey bee mating optimization algorithm, artificial neural network, MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence models performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model. (shrink)

Background: The proprioceptive neuromuscular facilitation reciprocal contraction pattern has the potential to increase the maximum rate of torque development. However, it is a more complex resistive exercise task and may interfere with improvements in the maximum rate of torque development due to motor skill learning, as observed for unidirectional contractions. The purpose of this study was to examine the cost-benefit of using the PNF exercise technique to increase the maximum rate of torque development.Methods: Twenty-six participants completed isometric (...) maximal extension-to-flexion or flexion-only contractions at the wrist. Ten of the assigned contractions were performed on each of three sessions separated by 48-h for skill acquisition. Retention was assessed with 5 contractions performed 2-weeks after acquisition. Torque and surface electromyographic activity were analyzed for evidence of facilitated contractions between groups, as well as alterations in muscle coordination assessed across test sessions. The criterion measures were: mean maximal isometric wrist flexion toque; the maximal rate of torque development ; root-mean-square error variability of the rate of torque versus torque phase-plane; the rate of wrist flexion muscle activation ; a coactivation ratio for wrist flexor and extensor sEMG activity; and wrist flexor electromechanical delay.Results: There were no significant differences between groups with respect to maximal wrist flexion torque, dτ/dtmax or RMSE variability of torque trajectories. Both groups exhibited a progressive increase in maximal strength and in dτ/dtmax from the start of acquisition to retention. RMSE was lowest after a 2-week rest interval. There were no significant differences between groups in the rate of muscle activation or the coactivation ratio. There was a reduction in coactivation that was retained after a 2-week rest interval. Alternatively, EMD was significantly greater in the experimental group across all sessions. However, both groups had a similar pattern of improvement to the third consecutive day of testing, but returned close to baseline value after the 2-week rest interval.Discussion: The wrist extension-to-flexion contraction pattern did not result in a greater maximal rate of torque development than simple contractions of the wrist flexors. There was no difference between groups with respect to motor skill learning. The main adaptation in neuromotor control was a decrease in coactivation, not the maximal rate of muscle activation. (shrink)