The relationship between anxiety and mathematics has often been investigated in the literature. Different forms of anxiety have been evaluated, with math anxiety (MA) and test anxiety (TA) consistently being associated with various aspects of mathematics. In this meta-analysis, we have evaluated the impact of these forms of anxiety, distinguishing between different types of mathematical tasks. In investigating this relationship, we have also included potential moderators, such as age, gender, working memory, type of task, and type of material. One hundred seventy-seven studies met the inclusion criteria, providing an overall sample of 906,311 participants. Results showed that both MA and TA had a significant impact on mathematics. Sociodemographic factors had modest moderating effects. Working memory (WM) also mediated the relationship between MA and TA with mathematics; however, this indirect effect was weak. Theoretical and educational implications, as well as future directions for research in this field, are discussed.

The consideration of intelligence has always been crucial for the diagnosis of specific learning disorders (SLDs). In particular, the hypothesis of a discrepancy between normal to high general intellectual abilities and poor academic achievement has traditionally been stressed (Mercer, Jordan, Allsopp, & Mercer, 1996). However, the traditional view of SLD has been criticized (e.g., Siegel, 1988). First of all, the dimensional distribution of academic and intellectual performances (i.e., a continuum of severity with no break points) has been emphasized, raising doubts on the use of specific cut points (Francis et al., 2005; see also Branum-Martin, Fletcher, & Stuebing, 2013). Another criticism concerns the fact that the discrepancy hypothesis treats intelligence as a unitary construct, contrasting a single, overall measure of intelligence (e.g., the full-scale intelligence quotient [FSIQ]) with achievement measures. However, many formulations of the construct of intelligence suggest that it can be better accounted for by considering different aspects (Carroll, 1993). Particularly in the case of children with SLD, using a battery of intelligence tests can help to detect strengths and weaknesses that could not emerge when a unitary IQ is considered (Giofrè & Cornoldi, 2015). In this respect, the different factor scores obtained using the Wechsler Intelligence Scale for Children-IV (WISC-IV; Wechsler, 2003)-that is, the most widely used tool for assessing children intelligence in the Western countries (Evers et al., 2012)-can be useful. Recent research has shown that the intellectual profile of children with SLD differs from that of typically developing (TD) children. In particular, it has been shown that

In the past two decades, psychological science has experienced an unprecedented replicability crisis which uncovered several problematic issues. Among others, the use and misuse of statistical inference plays a key role in this crisis. Indeed, statistical inference is too often viewed as an isolated procedure limited to the analysis of data that have already been collected. Instead, statistical reasoning is necessary both at the planning stage and when interpreting the results of a research project. Based on these considerations, we build on and further develop an idea proposed by Gelman and Carlin (2014) termed "prospective and retrospective design analysis".Rather than focusing only on the statistical significance of a result and on the classical control of type I and type II errors, a comprehensive design analysis involves reasoning about what can be considered a plausible effect size. Furthermore, it introduces two relevant inferential risks: the exaggeration ratio or Type M error (i.e., the predictable average overestimation of an effect that emerges as statistically significant), and the sign error or Type S error (i.e., the risk that a statistically significant effect is estimated in the wrong direction). design analysis is that it can be usefully carried out both in the planning phase of a study and for the evaluation of studies that have already been conducted, thus increasing researchers awareness during all phases of a research project. To illustrate the benefits of design analysis to the widest possible audience, we use a familiar example in psychology where the researcher is interested in analyzing the differences between two independent groups considering Cohens d as an effect size measure. We examine the case in which the plausible effect size is formalized as a single value, and propose a method in which uncertainty concerning the magnitude of the effect is formalized via probability distributions. Through several examples and an application to a real case study, we show that even though a design analysis requires big effort, it has the potential to contribute to planning more robust and replicable studies. Finally, future developments in the Bayesian framework are discussed. Keywordsprospective and retrospective design analysis, Type M and Type S errors, effect size, power, psychological research, statistical inference, statistical reasoning, R functions "If statisticians agree on one thing, it is that scientific inference should not be made mechanically." Gigerenzer and Marewski (2015, p. 422) "Accept uncertainty. Be thoughtful, open, and modest.

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Whereas a link between working memory (WM) and memory distortions has been demonstrated, its influence on emotional false memories is unclear. In two experiments, a verbal WM task and a false memory paradigm for negative, positive or neutral events were employed. In Experiment 1, we investigated individual differences in verbal WM and found that the interaction between valence and WM predicted false recognition, with negative and positive material protecting high WM individuals against false remembering; the beneficial effect of negative material disappeared in low WM participants. In Experiment 2, we lowered the WM capacity of half of the participants with a double task request, which led to an overall increase in false memories; furthermore, consistent with Experiment 1, the increase in negative false memories was larger than that of neutral or positive ones. It is concluded that WM plays a critical role in determining false memory production, specifically influencing the processing of negative material.