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This strategy resulted in a large amount of incorrect answers to spatial schema trials (day 1, % correct retrieval responses, mean ± s.e.m.: excluded subjects: 45.8 ± 1.7; included subjects: 89.6 ± 3.4), while the non-spatial schema was learned correctly (excluded subjects: 79.7 ± 3.1; included subjects: 93.4 ± 1.1).
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From the retrieved text data, there was a large amount of numerals, symbols, or grammatically incorrect words filtered out and the rest of the data were normalized into their pronounced form by additional processing, such as word tokenization, sentence segmentation, numerals transcription, and abbreviations expanding.
Barnes et al.[ 10] investigate a range of methods for handling missing data in their trial and conclude that imputing missing data as smokers "can cause a large amount of bias if imputing smoking is an incorrect assumption".
A large amount of information in free text is available that modifies the coded date or discloses incorrect classification as a case, even for 'hard' outcomes such as ovarian cancer, which is considered well documented in primary care records.
After determining whether a response was correct or incorrect, they should make a bet of either a small or large amount of money on the accuracy judgment.
Although both of these procedures may seem sensible, a large amount of research has showing that using trial data to choose the method of analysis can lead to biased results and incorrect type I error rates [ 21- 24].
"There's a large amount of pride".
You amassed a large amount of debt.
I spent a large amount of money.
"There's a large amount of support.
Large amount of floor space needed.
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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