“Rigged” Data Questions in Business Communication

Without getting too political, we could talk with students about what “rigged” data might look like in a business setting. President Trump used the term to explain his firing of the Bureau of Labor Statistics (BLS) commissioner.

Students might first review the BLS report, “The Employment Situation,” in question. Then, they might read an interview with Cornell University Economist Erica Groshen, who was the BLS commissioner for four years during President Obama’s administration.

Data integrity—perhaps the opposite of what President Trump meant by rigged—refers to its accuracy, completeness, and consistency over time. In the interview, Groshen disputes claims by highlighting the BLS’s rigor (choreographed, specific roles) and repetition (consistency):

With regard to allegations of altering the data, the process is highly, highly choreographed, with tight deadlines. BLS does this every month, and everybody knows who needs to do what job on what day to get this out on time.

Without getting into the details of the jobs report, students might explore potential “rigged” data in other contexts. What does “rigging” mean? Although a colloquial term, we could interpret it to mean falsifying or manipulating inputs or presenting results to intentionally mislead.

Some examples are obvious, but others are not so clear-cut. For example, at what point could “apple polishing,” “cherry picking,” or “comparing apples to oranges,” legitimately be labeled rigging data? If one month of weak sales data during a product recall is omitted from a line chart, is that rigging the data? How about If a rural location is compared to an urban location? On a dating app profile, if someone claims to be 5’ 11” when they are 5’ 10”, is that rigging data? What if they’re 5’ 10.2'“?

Students might consider the consequences of data reporting. Manipulating drug testing results is clearly different from exaggerating customer feedback about a food truck start-up. Students might discuss plans to ensure accuracy in their own data reports—and the consequences of inaccuracies or omissions.