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However, an in-depth analysis of the confusion tables reveals as this result is mainly biased by the HOG descriptor, operating on the unbalanced dataset by means of non-scaled data, bad outcomes (48.3 %).
Nevertheless, appearance models are not useful in case of noisy data, bad contrast or objects too far in the scene, but the general object model utilised in the proposed approach, together with a proper management of possible hypotheses, allows to better respond to these situations.
Six trials [ 19, 22, 28- 31] excluded the cases who had incomplete data, bad compliance or drop-out.
He discusses the most crucial flaws in research: bias, proxy endpoints, insufficient power, reporting of only positive data, bad study design, incorrect study design, missing data, short follow-up, and low statistical power.
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Bad data means bad results.
The converse is also true: "bad data" (big or small) makes for poor decision making.
The Data: No Bad Data This Week?
"The expenditure data looked bad but not dreadful.
The forward-looking data was bad as well.
If the data were bad, Dead Hand would try to communicate with central command.
"Mixing good quality data with bad quality data in this way is highly problematic and significantly weakens confidence in the findings of the current analysis".
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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