Answer is C:\ Overgeneralization in data analysis occurs when conclusions or insights are drawn from a dataset that are too broad or universal, based on a limited sample or specific conditions.
C. We may, for example, predict the outcome of something based on just one instance of it: After going on a job interview and finding out we didn't get the job, we conclude we'll never get a job (overgeneralizing) and feel hopeless about our career, leading to sadness and depression
Overgeneralization happens when you assume what you are seeing in your dataset is what you would see if you looked any other dataset meant to assess the same information, despite the fact that your data is very small or sometimes it's selected subset.
C. Overgeneralization. This occurs when inferences are made concerning
a general data population that leads to poor conclusions; for example,
extrapolating limited experiences and evidence to broad generalizations
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