Data Literacy Series Search

Poor data quality can result in unreliable analysis, inaccurate conclusions, and wasted effort. Since 'quality' is broad and often subjective, we break it down into key dimensions—each with guiding questions to help evaluate critical attributes of tabular data.

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Real-world datasets often contain missing values, a problem not always avoidable, even in well-designed research. Missing data should be handled carefully; otherwise, they may skew your analysis and compromise your results.

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