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P2-29: Data Quality’s Role in Sound Inference: Critical, Yet Commonly Ignored in Teaching and Practice, and Due for Increased Emphasis

By Sylvia Kuzmak, Rise Coaching and Consulting LLC, NJ


Data quality’s role in sound inference is critical, as expressed by the saying, “Garbage in, garbage out.” Assessment of data quality is dependent on the questions being addressed, and ensured through appropriate data collection procedures given the questions addressed.  Three illustrations of the trend of diminishing emphasis on the importance of data quality in teaching are provided: (1) diminished emphasis in the GAISE College Report from 2005 to 2016, (2) CAUSE cartoon contest winning caption overlooking data collection procedures and overvaluing data cleaning, and (3) published model class exercises overlooking basic data quality principles. With the rise of big data and concomitant lack of control and involvement in details of data collection by the analyst, the breadth of importance of data quality is commonly ignored. Four ways to provide increased emphasis on data quality in teaching are identified, expressed through reference to the three illustrations, and drawing from Kuzmak (2016).

Poster Session - P2-29 - Data Quality's Role in Sound Inference.pdf