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This study aimed to determine how watching lecture videos at 1× and 2× speeds affects memory retention in medical students. A posttest-only experimental design was utilized. The participants were 60 Year-1 and Year-2 medical students. The participants were assigned to one of two groups through stratified randomization: Group 1 would watch the video at 1× speed, and Group 2 at 2× speed. Their performance was assessed using a test comprising 20 multiple-choice questions. The test has been applied immediately after watching the video (Immediate test), and, again after one week (Delayed test). Parametric and non-parametric statistical tests were performed. In the Immediate test, the mean score of the 1× speed group was 11.26 ± 4.06, while 2× speed group's mean score was 10.16 ± 2.46. The difference was not significant t(58) = 1.26, p >.05. In the Delayed test, the mean score of 1× speed group was 9.66 ± 3.94, while 2× speed group's mean score was 8.36 ± 2.80. The difference was not significant t(55) = 1.42, p >.05. Watching the video lecture at 2× speed did not impair memory retention in medical students. This may help students to save time in their dense curricula. Practice points Many medical students watch lecture videos at 2× (double) speed to save time. While this is a time-saver, the negative impact on their learning is unclear. This paper describes a randomized trial that measures the learning impact when medical students watch an instructional video at 2× speed and finds that there is no significant negative impact on their learning. [ABSTRACT FROM AUTHOR]

Traditional outlier detection methods such as the minimum volume ellipsoid method and MCD method are all based on normal distance. As is well known, the norm-based distance is only effective in detecting difference with dense signals. However, these existing approaches will encounter detection power loss under the sparse signals settings. In this paper, we try to solve the problem of detecting outliers under the sparsity assumption by adapting the thresholding method. The proposed outlier detection procedure finds the clean set by the minimum diagonal product algorithm, then the maximum thresholding statistic is employed to identify the outlier. The finite sample performance of our method is evaluated through simulations. Compared with the existing outlier detection methods, simulation results show that our proposed outlier detection procedure is very efficient under sparse settings. [ABSTRACT FROM AUTHOR]

It is well known that ifXr: nandYr: nare order statistics from the i.i.d. samples with distribution functions F and G, respectively, such that F succeeds G in the convex order then. The result was extended to the generalized order statistics, and is also valid for their convex combinations. In this paper, we show that analogous relations hold true for much larger classes of linear combinations of generalized order statistics. [ABSTRACT FROM PUBLISHER]