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The reported effects were unchanged when the rejection proportion variables were transformed using either arcsine or logit transformations following Jaeger (2008).
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The next two rows display the rejection proportions when the hazard of discharge is the same, but the mortality hazard differs between the two groups.
HR dis = hazard ratio of discharge; HR mor = hazard ratio of mortality The next five rows of Table 5 display the rejection proportions when the hazard of discharge is different between the two groups, for varying hazard ratios of mortality.
When the null hypothesis is true (Λ1 = Λ2 = 1), all methods have rejection proportions close to the preset significance level 0.05 (For power values, see Additional file 1: Power plots of Figures S1-S5 for Tables 2-6); thindicatesthatthey they can control type I error rate.
The first row of Table 5 displays the rejection proportions for the four methods when the hazards of both mortality and discharge are equal between the two patient groups (i.e., the null hypothesis is true), using an α level of 0.05.
To check for order and learning effects, RTs of correct hits and rejections and proportion of correct and false hits were analysed separately with repeated measures ANOVAs with Treatment Order as between-subjects factor, and Session, Response Type (new vs. old words), Correctness of Response (correct vs. false hits) and Valence (emotional vs. neutral) as within-subjects.
After 10 000 replications, the 5% rejection rates (the proportion of times p<0.05) were 5.1% (gamma distributions) and 4.9% (lognormal distributions) for the t-test.
Interestingly, transfer of 10 or 5 × 10 couldestillld still prevent rejection in a proportion of recipients (n = 5, MST = 49 days; n = 5, MST = 57.6 days, respectively. Fig. 1D).
A rejection of insane proportions.
The protocol, like the currency, is a fiction they accept as real, because rejection by a large proportion of users be they banks, exchanges, speculators or miners could cause the whole system to collapse.
Furthermore, respondents that publish in a greater number of high-IF journals generally recommend rejection of a higher proportion of the manuscripts reviewed (Fig. 1a).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com