We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries. T he lack of reproducibility of scientific studies has caused growing concern over the credibility of claims of new discoveries based on 'statistically significant' findings. There has been much progress toward documenting and addressing several causes of this lack of reproducibility (for example, multiple testing, P-hacking, publication bias and under-powered studies). However, we believe that a leading cause of non-reproducibility has not yet been adequately addressed: statistical standards of evidence for claiming new discoveries in many fields of science are simply too low. Associating statistically significant findings with P < 0.05 results in a high rate of false positives even in the absence of other experimental, procedural and reporting problems.For fields where the threshold for defining statistical significance for new discoveries is P < 0.05, we propose a change to P < 0.005. This simple step would immediately improve the reproducibility of scientific research in many fields. Results that would currently be called significant but do not meet the new threshold should instead be called suggestive. While statisticians have known the relative weakness of using P ≈ 0.05 as a threshold for discovery and the proposal to lower it to 0.005 is not new 1,2 , a critical mass of researchers now endorse this change.We restrict our recommendation to claims of discovery of new effects. We do not address the appropriate threshold for confirmatory or contradictory replications of existing claims. We also do not advocate changes to discovery thresholds in fields that have already adopted more stringent standards (for example, genomics and high-energy physics research; see the 'Potential objections' section below).We also restrict our recommendation to studies that conduct null hypothesis significance tests. We have diverse views about how best to improve reproducibility, and many of us believe that other ways of summarizing the data, such as Bayes factors or other posterior summaries based on clearly articulated model assumptions, are preferable to P values. However, changing the P value threshold is simple, aligns with the training undertaken by many researchers, and might quickly achieve broad acceptance.

In an attempt to eliminate similar item content as an alternative explanation for the relation between depression and rumination, a secondary analysis was conducted using the data from S. Nolen-Hoeksema, J. Larson, and C. Grayson (1999). After constructing a measure of rumination unconfounded with depression content, support for a two factor model of rumination was found. These analyses indicate that the 2 components, reflective pondering and brooding, differentially relate to depression in terms of predictive ability and gender difference mediation. The results presented here support the general premise of Nolen-Hoeksema's Response Styles Theory (S. Nolen-Hoeksema 1987) that rumination can contribute to more depressive symptoms and to the gender difference in depression, but suggest important refinements of the theory. Such refinements include the need to differentiate between the reflective pondering component of rumination and the brooding component in rumination research.

No abstract

When individuals choose among risky alternatives, the psychological weight attached to an outcome may not correspond to the probability of that outcome. In rank-dependent utility theories, including prospect theory, the probability weighting function permits probabilities to be weighted nonlinearly. Previous empirical studies of the weighting function have suggested an inverse S-shaped function, first concave and then convex. However, these studies suffer from a methodological shortcoming: estimation procedures have required assumptions about the functional form of the value and/or weighting functions. We propose two preference conditions that are necessary and sufficient for concavity and convexity of the weighting function. Empirical tests of these conditions are independent of the form of the value function. We test these conditions using preference "ladders" (a series of questions that differ only by a common consequence). The concavity-convexity ladders validate previous findings of an S-shaped weighting function, concave up to pdecision making, expected utility, nonexpected utility theory, prospect theory, risk, risk aversion

We show that differences in social orientation and in cognition that exist between cultures and social classes do not necessarily have counterparts in individual differences within those groups. Evidence comes from a large-scale study conducted with 10 measures of independent vs. interdependent social orientation and 10 measures of analytic vs. holistic cognitive style. The social measures successfully distinguish between interdependence (viewing oneself as embedded in relations with others) and independence (viewing oneself as disconnected from others) at the group level. However, the correlations among the measures were negligible. Similar results were obtained for the cognitive measures, for which there are no coherent individual differences despite the validity of the construct at the group level. We conclude that behavioral constructs that distinguish among groups need not be valid as measures of individual differences.

Many research problems in psychology require statistical methods that take into account the dependencies introduced by dyadic interaction. The authors provide correlational tools for dyadic data when the individuals within the dyads are both from the same class or category, such as 2 male adults. First, the authors provide significance tests for correlations between 2 variables when individuals are nested within dyads. Second, they provide a simplified method for decomposing the overall correlation into individual-level and dyad-level relations. Finally, the authors demonstrate these methods with dyadic data collected by L. Stinson and W. Ickes (1992) in a study of unstructured dyadic interactions.There are many problems in psychology for which theory and data analysis at the level of the dyad, rather than the individual, are appropriate. Social psychologists talk about warm and cold interactions, as well as about warm and cold individuals. Clinical psychologists talk about happy and unhappy couples, as well as about happy and unhappy people. Developmental psychologists talk about cooperative and competitive sibling relationships, as well as about cooperative and competitive siblings. However, rather than collect data at the dyad level, researchers typically rely on data collected from the individuals within these dyads. In the case of correlational analyses, the nonindependent nature of dyadic data presents both statistical and conceptual challenges.These challenges can be seen in a simple example. Stinson and Ickes (1992) observed 24 pairs of male students interacting in an unstructured waiting room situation. Each interaction was videotaped and coded on a number of dimensions, including the number of verbalizations and the number of gestures made by each individual. The researchers were interested in, among other things, the correlation between the frequency of students' verbalizations and gestures. Note that although the researchers had 48 individual scores on each variable to correlate, these scores were collected from 24 interdependent dyads. In

Prior research indicates that East Asians are more sensitive to contextual information than Westerners. This article explored aesthetics to examine whether cultural variations were observable in art and photography. Study 1 analyzed traditional artistic styles using archival data in representative museums. Study 2 investigated how contemporary East Asians and Westerners draw landscape pictures and take portrait photographs. Study 3 further investigated aesthetic preferences for portrait photographs. The results suggest that (a) traditional East Asian art has predominantly context-inclusive styles, whereas Western art has predominantly object-focused styles, and (b) contemporary members of East Asian and Western cultures maintain these culturally shaped aesthetic orientations. The findings can be explained by the relation among attention, cultural resources, and aesthetic preference.

Despite publication of many well-argued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to be so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) that has a justifiable mapping onto confidence in repeatability of a null hypothesis rejection. The most-criticized flaws of NHT can be avoided when the importance of a hypothesis, rather than the p value of its test, is used to determine that a finding is worthy of report, and when p approximately equal to .05 is treated as insufficient basis for confidence in the replicability of an isolated non-null finding. Together with many recent critics of NHT, we also urge reporting of important hypothesis tests in enough descriptive detail to permit secondary uses such as meta-analysis.