Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β 1 is larger than β 2 and β 3. The corresponding hypothesis is H : β 1 > {β 2, β 3 } and this is known as an (order) constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a (...) smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a pre-specified power (say, 0.80) for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30–50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., β 1 > β 2 ) results in a higher power than assigning a positive or a negative sign to the parameters (e.g., β 1 > 0). (shrink)

Frequentist inference typically is described in terms of hypothetical repeated sampling but there are advantages to an interpretation that uses a single random sample. Contemporary examples are given that indicate probabilities for random phenomena are interpreted as classical probabilities, and this interpretation of equally likely chance outcomes is applied to statistical inference using urn models. These are used to address Bayesian criticisms of frequentist methods. Recent descriptions of p-values, confidence intervals, and power are viewed through the lens of (...) classical probability based on a single random sample from the population. (shrink)

Several studies have shown that our visual system may construct a “summary statistical representation” over groups of visual objects. Although there is a general understanding that human observers can accurately represent sets of a variety of features, many questions on how summary statistics, such as an average, are computed remain unanswered. This study investigated sampling properties of visual information used by human observers to extract two types of summary statistics of item sets, average and variance. We presented three (...) models of ideal observers to extract the summary statistics: a global sampling model without sampling noise, global sampling model with sampling noise, and limited sampling model. We compared the performance of an ideal observer of each model with that of human observers using statistical efficiency analysis. Results suggest that summary statistics of items in a set may be computed without representing individual items, which makes it possible to discard the limited sampling account. Moreover, the extraction of summary statistics may not necessarily require the representation of individual objects with focused attention when the sets of items are larger than 4. (shrink)

Statistics in public life .................................................................................................... .....5 Things and numbers............................................................................................. ...................8 Representative samples............................................................................................. ..........8 Averages: meaning and relevance .....................................................................................9 Correlations........................................................................................ ................................10 Applied statistics .................................................................................................... ................13 Relative risks .................................................................................................... ..................14 Relative risk versus absolute risk.....................................................................................16 Problems of classification and confounding factors....................................................17 Epidemiological research............................................................................................ ..........19 Publication bias................................................................................................ ..................20 Statistical significance versus scientific relevance................................................................24 Relative risk again............................................................................................... ...............24 P-values............................................................................................ ...................................25 Confidence intervals .................................................................................................... .....26 Correlation is not causation .............................................................................................26 An infamous episode .................................................................................................... ....27 Terror, utopianism and power .............................................................................................29 Faith and science .................................................................................................... ...........29 Fear and power: the precautionary principle.................................................................30 Utopian salvation........................................................................................... ....................32....

Decisions in the environmental and in particular the climate domain are burdened with uncertainty. Here, we focus on uncertainties faced by individuals when making decisions about environmental behavior, and we use the statistical sampling framework to develop a classification of different sources of uncertainty they encounter. We then map these sources to different public policy strategies aiming to help individuals cope with uncertainty when making environmental decisions.

This study investigates a text excerpt containing 20,000 Russian letters of the alphabet, excluding $\Cprime$ and $\Cdprime$, from Pushkin's novel Eugene Onegin–the entire first chapter and sixteen stanzas of the second.

The social sciences face a problem of sample non-representation, where the majority of samples consist of undergraduate students from Euro-American institutions. The problem has been identified for decades with little trend of improvement. In this paper, I trace the history of sampling theory. The dominant framework, called the design-based approach, takes random sampling as the gold standard. The idea is that a sampling procedure that is maximally uninformative prevents samplers from introducing arbitrary bias, thus preserving sample representation. (...) I show how this framework, while good in theory, faces many challenges in application. Instead, I advocate for an alternative framework, called the model-based approach to sampling, where representative samples are those balanced in composition, however they were drawn. I argue that the model-based framework is more appropriate in the social sciences because it allows for systematic assessment of imperfect samples and methodical improvement in resource-limited scientific contexts. I end with practical proposals of improving sample quality in the social sciences. (shrink)

Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations. How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical (...) methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained at-least-one significant effect, yet most papers reported multiple effects, and the number of non-significant effects did not impact the score. Thus, the predictiveness score was rarely less than the maximum. In addition, only significant effects were included in Endsley’s quantitative synthesis. Filtering excluded half of all reported effects, with guaranteed minimum effect sizes based on sample size. Results for filtered compared to as-reported effects clearly diverged. Compared to the mean of as-reported effects, the filtered mean was overestimated by 56%. Furthermore, 92% of the as-reported effects were below the mean of filtered effects. We conclude that outcome-dependent selection of effects is circular, predetermining results and running contrary to the purpose of meta-analysis. Instead of using significance to score and filter effects, meta-analyses should follow established research practices. (shrink)

I have conducted a similar investigation on a text by a different author. The results of this investigation, which was performed on a text passage of 100,000 letters, are presented in the following tables from which one can see how and to what extent the limit theorems of the calculus of probability actually become evident.

To date researchers planning experiments have always lived by the mantra that 'using equal sample sizes gives the best results' and although unequal groups are also used in experimentation, it is not the preferred method of many and indeed actively discouraged in literature. However, during live study planning there are other considerations that we must take into account such as availability of study participants, statistical power and, indeed, the cost of the study. These can all make allocating equal sample (...) sizes difficult, and sometimes near impossible. This, some might say, means that the study would not adhere to rigorous statistical standard (Rosenbaum and Rubin, 1985). However, here we present evidence that, not only is this a false assumption, but that we may actually gain more power in the study by actually using unequal groups. Here, data from a Sepsis Biomarker study is used, in which the aim is to predict, by biomarker level and presence, whether the patient would go on to develop sepsis. It was found that larger control groups may give more power to studies looking for an effect in the mid range but not for large or small effects. This study shows merit in the hypothesis that more power can be achieved when a larger control group is used. Published by the Ministry of Defence at dstl - Reference DSTLTR92592 P2PP2R-2016-02-23T13:39:45. (shrink)

Statistical inference is often justified by long-run properties of the sampling distributions, such as the repeated sampling rationale. These are frequentist justifications of statistical inference. I argue, in line with existing philosophical literature, but against a widespread image in empirical science, that these justifications are flawed. Then I propose a novel interpretation of probability in statistics, the artefactual interpretation. I believe that this interpretation is able to bridge the gap between statistical probability calculations and rational (...) decisions on the basis of observed data. The artefactual interpretation is able to justify statistical inference without making any assumptions about probability in the material world. (shrink)

Introduction: Publications arguing against the null hypothesis significance testing (NHST) procedure and in favor of good statistical practices have increased. The most frequently mentioned alternatives to NHST are effect size statistics (ES), confidence intervals (CIs), and meta-analyses. A recent survey conducted in Spain found that academic psychologists have poor knowledge about effect size statistics, confidence intervals, and graphic displays for meta-analyses, which might lead to a misinterpretation of the results. In addition, it also found that, although the use of (...) ES is becoming generalized, the same thing is not true for CIs. Finally, academics with greater knowledge about ES statistics presented a profile closer to good statistical practice and research design. Our main purpose was to analyze the extension of these results to a different geographical area through a replication study. Method: For this purpose, we elaborated an on-line survey that included the same items as the original research, and we asked academic psychologists to indicate their level of knowledge about ES, their CIs, and meta-analyses, and how they use them. The sample consisted of 159 Italian academic psychologists (54.09% women, mean age of 47.65 years). The mean number of years in the position of professor was 12.90 (SD = 10.21). Results: As in the original research, the results showed that, although the use of effect size estimates is becoming generalized, an under-reporting of CIs for ES persists. The most frequent ES statistics mentioned were Cohen’s d and R2/η2, which can have outliers or show non-normality or violate statistical assumptions. In addition, academics showed poor knowledge about meta-analytic displays (e.g., forest plot and funnel plot) and quality checklists for studies. Finally, academics with higher-level knowledge about ES statistics seem to have a profile closer to good statistical practices. Conclusions: Changing statistical practice is not easy. This change requires statistical training programs for academics, both graduate and undergraduate. (shrink)

With his new riddle of induction, Goodman raised a problem for enumerative induction which many have taken to show that only some ‘natural’ properties can be used for making inductive inferences. Arguably, however, (i) enumerative induction is not a method that scientists use for making inductive inferences in the first place. Moreover, it seems at first sight that (ii) Goodman’s problem does not affect the method that scientists actually use for making such inferences—namely, classical statistics. Taken together, this would indicate (...) that (iii) ‘naturalness’ is not such a relevant concept for the problem of induction, after all. I have no objections against (i) and (iii). But, contrary to (ii), I argue that classical statistics does face a version of Goodman’s problem, which I call the statistical riddle of induction. Given that his problem merely is an instance of the more general problem of underdetermination, this is hardly surprising. Perhaps more importantly, I also show how my riddle can be used for exposing a common fallacy in probabilistic reasoning, without relying on Bayesian assumptions. (shrink)

Intentional sampling methods are non-probabilistic procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. Intentional sampling methods are intended for exploratory research or pilot studies where tight budget constraints preclude the use of traditional randomized representative sampling. The possibility of subsequently generalize statistically from such deterministic samples to the general population has been the issue of long standing arguments and debates. Nevertheless, the intentional sampling techniques developed in (...) this paper explore pragmatic strategies for overcoming some of the real or perceived shortcomings and limitations of intentional sampling in practical applications. (shrink)

NHSTP is embedded in the research of “cognitive science.” Its use is based on unstated assumptions about the practices of sampling, “operationalizing,” and using group data. NHSTP has facilitated both research and theorizing – research findings of limited interest – diverse theories that seldom complement one another. Alternative methods are available for data acquisition and analysis, and for assessing the “truth- value” of generalizations.

Nonbelievers represent an understudied population in Greece. This investigation reports on the translation, cultural adaptation, and initial validation of the Nonreligious-Nonspiritual Scale (NRNSS), a measure designed to assess nonbelief. Data from 1754 participants were collected to examine the psychometric properties of the Greek version of the instrument and to assess the nationwide interpretability of the measure. Factor analyses suggested that the 16-item scale retained its bifactor model. Convergent validity was supported through associations with additional measures, namely, the Meaning in Life (...) Questionnaire (MLQ) and the Connor–Davidson Resilience Scale (CD-RISC), which were used as reference criteria. Potential utility of the measure and future directions for ongoing development are discussed. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement (...) Bayesian inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper describes two (...) elementary statistical difficulties for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. One difficulty obtains if large numbers of cells are aggregated in a measurement of expression levels from a common population of cells; the other obtains if small numbers of cells are aggregated or if samples are separately aggregated over different populations of cells. (shrink)

Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a (...) class='Hi'>statistical inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets. (shrink)

Infants use statistical information in their environment, as well as others’ emotional communication, to understand the intentions of social partners. However, rarely do researchers consider these two sources of social information in tandem. This study assessed 2-year-olds’ attributions of intentionality from non-random sampling events and subsequent discrete emotion reactions. Infants observed an experimenter remove five objects from either the non-random minority (18%) or random majority (82%) of a sample and express either joy, disgust, or sadness after each selection. (...) Two-year-olds inferred the experimenter’s intentionality by giving her the object that she had previously selected when she expressed joy or disgust after non-random sampling events, but not when she expressed sadness or sampled at random. These findings demonstrate that infants use both statistical regularities and discrete emotion communication to infer an agent’s intentions. In particular, the present findings show that 2-year-olds infer that an agent can intentionally select a preferred or an undesired object from a sample as a function of the discrete emotion. Implications for the development of inferring intentionality from statistical sampling events and discrete emotion communication are discussed. (shrink)

The dissociation between statistical prediction and scientific explanation advanced by Bowers et al. for studies of vision using deep neural networks is also observed in several other domains of behavior research, and is in fact unavoidable when fitting large models such as deep nets and other supervised learners, with weak theoretical commitments, to restricted samples of highly stochastic behavioral phenomena.

Statistics and probability enabled students to better understand, process, and evaluate massive amounts of quantitative data that existed and had a probabilistic sense in uncertain situations. The research article aimed to elucidate the performance and self-efficacy as predictors of students' achievement in the statistics and probability courses. The study utilized a descriptive-predictive research method and was conducted at Sto. Tomas National High School, involving a sample of 263 grade 11 senior high school students. The gathered data were analyzed using descriptive (...) measures and multiple regression analysis. The study's results revealed that the performance in General Mathematics was very satisfactory, while self-efficacy was high. Moreover, the level of achievement in Statistics and Probability was very satisfactory. It was also revealed that both General Mathematics performance and self-efficacy had a positive and significant relationship with Statistics and Probability achievement. Through regression analysis, it was discovered that General Mathematics performance was the strongest predictor that influenced achievement in Statistics and Probability. The study identified a significant predictive model for Statistics and Probability achievement. These findings could provide valuable guidance to teachers in enhancing the achievement of senior high school students in Statistics and Probability. (shrink)

Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper describes two (...) elementary statistical difficulties for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. One difficulty obtains if large numbers of cells are aggregated in a measurement of expression levels from a common population of cells; the other obtains if small numbers of cells are aggregated or if samples are separately aggregated over different populations of cells. (shrink)

An inductive logic is a system of inference that describes the relation between propositions on data, and propositions that extend beyond the data, such as predictions over future data, and general conclusions on all possible data. Statistics, on the other hand, is a mathematical discipline that describes procedures for deriving results about a population from sample data. These results include predictions on future samples, decisions on rejecting or accepting a hypothesis about the population, the determination of probability assignments over such (...) hypotheses, the selection of a statistical model for studying the population, and so on. Both inductive logic and statistics are calculi for getting from the given data to propositions or results that transcend the data. (shrink)

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. Statistical significance means only that chance influences can be excluded as an explanation of data; it does not identify the nonchance factor responsible. The experimental conclusion is drawn with the inductive principle underlying the experimental design. A chain of deductive arguments gives rise to the theoretical conclusion via the experimental conclusion. The anomalous relationship between statistical significance and the effect size often used to criticize NHSTP is more apparent than real. The absolute size of the effect is not an index of evidential support for the substantive hypothesis. Nor is the effect size, by itself, informative as to the practical importance of the research result. Being a conditional probability, statistical power cannot be the apriori probability of statistical significance. The validity of statistical power is debatable because statistical significance is determined with a single sampling distribution of the test statistic based on H0, whereas it takes two distributions to represent statistical power or effect size. Sample size should not be determined in the mechanical manner envisaged in power analysis. It is inappropriate to criticize NHSTP for nonstatistical reasons. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data. Neither can any of them fulfill the nonstatistical functions expected of them by critics. (shrink)

When using sample data to decide whether two populations differ, laypeople attend to the difference between group means, but largely overlook within-group variability (Obrecht, Chapman, & Gelman, 2007). We show, first, that laypeople know about and use story-implied variability when making pairwise comparisons. Then we demonstrate that participants' sensitivity to variance in a dataset is boosted when presented in a context that implies consistent variance information. Statistical data were couched in stories about electrical conductivity measurements obtained from element samples (...) (low-variability category) or body weight measurements from samples of peoples (high-variability category). We manipulated, between participants, whether the data variance matched or mismatched the story-implied variability. Participants who received data in a matching context showed high sensitivity to variance, while those in the mismatching condition did not. Laypeople use statistical data to make reasonable inferences when those data are provided in a context that makes sense. (shrink)

I dwell on the appearance, in the mid-19th century, of a formula describing random sampling without replacement under incomplete knowledge and discuss the statistical aspect of this formula and its applications. I also provide illustrations showing that the fairness of sampling without replacement was being questioned.

The Condorcet efficiency of a social choice procedure is usually defined as the probability that this procedure coincides with the majority winner (or majority ordering) in random samples, given a majority winner exists (or given the majority ordering is transitive). Consequently, it is in effect a conditional probability that two sample statistics coincide, given certain side conditions. We raise a different issue of Condorcet efficiencies: What is the probability that a social choice procedure applied to a sample matches with the (...) majority preferences of the population from which the sample was drawn? We investigate the canonical case where the sample statistic is itself also majority rule and the samples are drawn from real world distributions gathered from national election surveys in Germany, France, and the United States. We relate the results to the existing literature on majority cycles and social homogeneity. We find that these samples rarely display majority cycles, whereas the probability that a sample misrepresents the majority preferences of the underlying population varies dramatically and always exceeds the probability that the sample displays cyclic majority preferences. Social homogeneity plays a fundamental role in the type of Condorcet efficiency investigated here. (shrink)

Popper argued that a statistical falsification required a prior methodological decision to regard sufficiently improbable events as ruled out. That suggestion has generated a number of fruitful approaches, but also a number of apparent paradoxes and ultimately, no clear consensus. It is still commonly claimed that, since random samples are logically consistent with all the statistical hypotheses on the table, falsification simply does not apply in realistic statistical settings. We claim that the situation is considerably improved if (...) we ask a conceptually prior question: when should a statistical hypothesis be regarded as falsifiable. To that end we propose several different notions of statistical falsifiability and prove that, whichever definition we prefer, the same hypotheses turn out to be falsifiable. That shows that statistical falsifiability enjoys a kind of conceptual robustness. These notions of statistical falsifiability are arrived at by proposing statistical analogues to intuitive properties enjoyed by exemplary falsifiable hypotheses familiar from classical philosophy of science. That demonstrates that, to a large extent, this philosophical tradition was on the right conceptual track. Finally, we demonstrate that, under weak assumptions, the statistically falsifiable hypotheses correspond precisely to the closed sets in a standard topology on probability measures. That means that standard techniques from statistics and measure theory can be used to determine exactly which hypotheses are statistically falsifiable. In other words: the proposed notion of statistical falsifiability both answers to our conceptual demands and submits to standard mathematical techniques. (shrink)

The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. It (...) is argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models. Introduction Models in statistics—‘of what population is this a random sample?’ The fundamental lemma Controversy about models Exploratory data analysis as a model-critical approach. (shrink)

For each of the N-values 2, 4, 8, 16, 32, 64, 128, 256, 512, and 1,024, 50,000 samples of size N were drawn from an L-shaped population, and for each sample the Z and t statistics were calculated. The resulting distributions of 50,000 Z or t values at each sample size were then used to study the robustness of left-tailed, right-tailed, and two-tailed Z and t tests at α levels of.05,.01, and.001 (and, for Z only,.0001). The actually obtained proportion, ρ, (...) of Type I errors was often far greater or far smaller than the nominal proportion, α. Furthermore, although α is the expected value of ρ at infinite N, no N-value below 512 ever brought the deviation of p from α to within 10% of α for any t tests or one-tailed Z tests. (shrink)

BackgroundThe experience sampling method is an increasingly popular data collection method to assess interpersonal dynamics in everyday life and emotions contextualized in real-world settings. As primary advantages of ESM sampling strategies include minimization of memory biases, maximization of ecological validity, and hypothesis testing at the between- and within-person levels, ESM is suggested to be appropriate for studying the daily lives of educational actors. However, ESM appears to be underutilized in education research. We, thus, aimed to systematically evaluate the (...) methodological characteristics and quality of published ESM studies of social interactions among children and adolescents in school settings, as well as to explore how much variance in social interaction variables could be attributed to the within-person level.MethodUsing Academic Search Complete, APA PsycINFO, APA PsycArticles, ProQuest, Web of Science, Wiley Online Library, and SAGE Journals, and in accordance with PRISMA guidelines and pre-defined eligibility criteria, we conducted a systematic literature search of experience sampling studies up to November 2020. To assess methodological quality, we used a modified checklist for reporting of ESM studies.ResultsOf the originally 2 413 identified studies, a final 52 experience sampling studies were included in the present review. Findings on sample and study design characteristics generally revealed wide variability. Even if high-quality studies were associated with higher scores on the training of participants in using the ESM procedure, and use of incentives, these design strategies did not reveal a statistically significant impact on compliance. The intraclass correlation coefficient was reported in nine studies and on average 58% of the variance in social interaction variables could be attributed to within-person fluctuation between timepoints.ConclusionThe current study is the first to systematically review ESM-based studies on social interactions among children and adolescents in the school context. These observations suggest that ESM is a potentially favorable technique for extracting complex social phenomena in real-world settings. We hope that this review will contribute to improving the quality assessment of ESM studies as well as to inform and guide future experience sampling studies, particularly regarding social phenomena with children and adolescents in educational settings. (shrink)

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new interesting (...) asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)

We analyse the issue of using prior information in frequentist statistical inference. For that purpose, we scrutinise different kinds of sampling designs in Jerzy Neyman’s theory to reveal a variety of ways to explicitly and objectively engage with prior information. Further, we turn to the debate on sampling paradigms (design-based vs. model-based approaches) to argue that Neyman’s theory supports an argument for the intermediate approach in the frequentism vs. Bayesianism debate. We also demonstrate that Neyman’s theory, by (...) allowing non-epistemic values to influence evidence collection and formulation of statistical conclusions, does not compromise the epistemic reliability of the procedures and may improve it. This undermines the value-free ideal of scientific inference. (shrink)