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.

Human cooperation is unparalleled in the animal world and rests on an altruistic concern for the welfare of genetically unrelated strangers. The evolutionary roots of human altruism, however, remain poorly understood. Recent evidence suggests a discontinuity between humans and other primates because individual chimpanzees do not spontaneously provide food to other group members, indicating a lack of concern for their welfare. Here, we demonstrate that common marmoset monkeys (Callithrix jacchus) do spontaneously provide food to nonreciprocating and genetically unrelated individuals, indicating that other-regarding preferences are not unique to humans and that their evolution did not require advanced cognitive abilities such as theory of mind. Because humans and marmosets are cooperative breeders and the only two primate taxa in which such unsolicited prosociality has been found, we conclude that these prosocial predispositions may emanate from cooperative breeding.cooperative breeding ͉ non-human primates ͉ prosociality ͉ helping ͉ dictator game

Conformity is a type of social learning that has received considerable attention among social psychologists and human evolutionary ecologists, but existing empirical research does not identify conformity cleanly. Conformity is more than just a tendency to follow the majority; it involves an exaggerated tendency to follow the majority. The "exaggerated" part of this definition ensures that conformists do not show just any bias toward the majority, but a bias sufficiently strong to increase the size of the majority through time. This definition of conformity is compelling because it is the only form of frequency-dependent social influence that produces behaviorally homogeneous social groups. We conducted an experiment to see if players were conformists by separating individual and social learners. Players chose between two technologies repeatedly. Payoffs were random, but one technology had a higher expected payoff. Individual learners knew their realized payoffs after each choice, while social learners only knew the distribution of choices among individual learners. A subset of social learners behaved according to a classic model of conformity. The remaining social learners did not respond to frequency information. They were neither conformists nor nonconformists, but mavericks. Given this heterogeneity in learning strategies, a tendency to conform increased earnings dramatically.