A Checklist for Analyzing Data

Understandably, when researchers get data, they are eager to learn what the data have to say regarding the main study hypotheses. This excitement, combined with looming deadlines, can result in researchers skipping the most important steps of a data analysis. For example, researchers might rush to run formal statistical tests that compare their groups before they have properly cleaned and vetted their data. In a proper data analysis, the bulk of an analyst's time is not spent on running final statistical models, but rather on tasks such as cleaning, checking, understanding, and plotting the data.

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