Calculus Is So Last Century
Tianhui Michael Li and Allison Bishop write about the overemphasis on calculus in high school and college math courses. Statistics, linear algebra and algorithmic thinking are more valuable in the digital age.
Send us a link
Tianhui Michael Li and Allison Bishop write about the overemphasis on calculus in high school and college math courses. Statistics, linear algebra and algorithmic thinking are more valuable in the digital age.
New Chinese 5-year plan promises 2.5% R&D:GDP ratio by 2020, up from 2.05% in 2014.
How do retractions influence the scholarly impact of retracted papers, authors, and institutions; and how does this influence propagate to the wider academic community through scholarly associations?
Jisc Digifest hears openness could bring benefits, but some cite plagiarism risks
Limited institutional resources mean that single parents often need a network of support to further their scientific careers.
An influential psychological theory, borne out in hundreds of experiments, may have just been debunked. How can so many scientists have been so wrong?
A look at the PLoS ONE paper on a hand designed by “the Creator”
New startups like this one are trying to disrupt traditional academic publishing.
Times Higher Education World University Rankings data reveal the 20 best institutions based on private-sector investment per academic
The psychology establishment is fighting back against an attack on its reliability. But it might be letting emotion get in the way.
Compared with psychology, the replication rate "is rather good," researchers say
Reanalysis of last year's enormous replication study argues that there is no need to be so pessimistic.
Apparently creationist research prompts soul searching over process of editing and peer review.
Observational study from 1994 to 2014
Universities need to expand international engagement to remain competitive, according to a report by Digital Science.
Female researchers now account for 37 per cent of first authors in medicine’s top journals, says US study
Recently, some have begun to explore the utilization of the crowd for various purposes in medical research, including fundraising as well as crowdsourcing for intellectual analyses and insights.
A report on international academic collaboration across the UK research base and on the implications of EU and global collaboration for universities, research assessment and the economy.
Researchers on social media ask at what point replication efforts go from useful to wasteful.
A peer-to-peer website aims to disrupt the author-services industry.
To engage the public in your work, whilst also solving that all-important research funding problem?
Researchers gathered at Sapienza University of Rome last week to discuss the cuts in Italy's research budget.
Fewer than half of academies have policies in place to boost gender equality in membership.
We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset ( N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.