52% Yes, a signiicant crisis 3% No, there is no crisis 7% Don't know 38% Yes, a slight crisis 38% Yes, a slight crisis 1,576 RESEARCHERS SURVEYED M ore than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature's survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research. The data reveal sometimes-contradictory attitudes towards reproduc-ibility. Although 52% of those surveyed agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature. Data on how much of the scientific literature is reproducible are rare and generally bleak. The best-known analyses, from psychology 1 and cancer biology 2 , found rates of around 40% and 10%, respectively. Our survey respondents were more optimistic: 73% said that they think that at least half of the papers in their field can be trusted, with physicists and chemists generally showing the most confidence. The results capture a confusing snapshot of attitudes around these issues, says Arturo Casadevall, a microbiologist at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. "At the current time there is no consensus on what reproducibility is or should be. " But just recognizing that is a step forward, he says. "The next step may be identifying what is the problem and to get a consensus. "

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One of the most difficult concepts for statistics students is the standard error of the mean. To improve understanding of this concept, 1 group of students used a hands-on procedure to sample from small populations representing either a true or false null hypothesis. The distribution of 120 sample means (n = 3) from each population had standard errors that closely approximated those of the theoretical sampling distributions, thereby illustrating how the Central Limit Theorem provides a standard error to use for hypothesis testing. Performance on an exam about the standard error of the mean was significantly better for the students who had completed this exercise than for students in a control group.

Background College students often do not retain what they learn in Statistics in order to apply it in Experimental Psychology. Self-explanation, that is, elaborating on what one is trying to learn by asking questions, making inferences, etc., improves learning and may improve retention. Objective The purpose of this study was to determine whether self-explanation was superior to students’ usual study methods specifically for learning some basic concepts in statistics, and, if so, if it was similarly useful for retention a semester after the initial learning. Method We used 199 college students as participants in a randomized, between participant, two-part experiment examining the effects of training by prompting self-explanations as a potential solution to this applied problem. Results The self-explanations that we elicited improved initial learning and were superior to students’ usual study methods, but did not benefit retention. Conclusions Future research on improving the quality of the self-explanations and training with spaced retrieval practice, in order to benefit retention, is suggested. Teaching Implication Self-explanation should be implemented for teaching statistics in order to benefit initial learning. However, teachers should explore other methods to accomplish retention.