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Five participants (1 HY, 3 HO, 2 MCI) were excluded because their accuracy on auditory trials in the detection task was <90%, indicating a potential hearing loss.
In this experiment, two participants were excluded and replaced because their accuracy on the letter search task was lower than 50%%.
Three participants were replaced because their accuracy on the letter search task was lower than 50%%, and two were replaced because they detected less than 30%% of the beeps in either or both of the load conditions.
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Predictions are important not because of their accuracy but because of the learning opportunities they present.
In the past decade, methods based on DFT have got the attention of researchers because of their accuracy and wide applications.
It is also unlikely that they are caused by reduced attention or poorer task-performance in children, because accuracy on the one-back task in the scanner was far above chance level and equivalently high across all ages and conditions.
Their accuracy is dependent on their underlying assumptions.
But their accuracy is not on the agenda.
Because ZiPS forecasts are well-known for their accuracy and freely available on the internet, they make for a formidable baseline.
Map hoaxers usually fall down not on their inaccuracy but on their accuracy.
As such, determining the accuracy of self-reported, health-related data has important implications for future delivery and accessibility of healthcare services, because the utility of these data depends on their accuracy.
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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