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.

Analysis of text from open-ended interviews has become an important research tool in numerous fields, including business, education, and health research. Coding is an essential part of such analysis, but questions of quality control in the coding process have generally received little attention. This article examines the text coding process applied to three HIV-related studies conducted with the Centers for Disease Control and Prevention considering populations in the United States and Zimbabwe. Based on experience coding data from these studies, we conclude that (1) a team of coders will initially produce very different codings, but (2) it is possible, through a process of codebook revision and recoding, to establish strong levels of intercoder reliability (e.g., most codes with kappa 0.8). Furthermore, steps can be taken to improve initially poor intercoder reliability and to reduce the number of iterations required to generate stronger intercoder reliability.

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In the aftermath of a decade-long Maoist civil war in Nepal and the recent relocation of thousands of Bhutanese refugees from Nepal to Western countries, there has been rapid growth of mental health and psychosocial support programs, including posttraumatic stress disorder (PTSD) treatment, for Nepalis and ethnic Nepali Bhutanese. This medical anthropology study describes the process of identifying Nepali idioms of distress and local ethnopsychology and ethnophysiology models that promote effective communication about psychological trauma in a manner that minimizes stigma for service users. Psychological trauma is shown to be a multi-faceted concept that has no single linguistic corollary in the Nepali study population. Respondents articulated different categories of psychological trauma idioms in relation to impact upon the heart-mind, brain-mind, body, spirit, and social status, with differences in perceived types of traumatic events, symptom sets, emotion clusters, and vulnerability. Trauma survivors felt blamed for experiencing negative events, which were seen as karma transmitting past life sins or family member sins into personal loss. Some families were reluctant to seek care for psychological trauma because of the stigma of revealing this bad karma. In addition, idioms related to brain-mind dysfunction contributed to stigma while heart-mind distress was a socially acceptable reason for seeking treatment. Different categories of trauma idioms support the need for multidisciplinary treatment with multiple points of service entry.