Quantitative analyses must yield results that are characterized by accuracy, dependability, and integrity. Accuracy relates to the results' correctness. If the data are valid and the arithmetic and formulas are both properly applied as well as meticulously checked, then one can conclude an answer is accurate. Dependability refers to whether results are reliable and stable. Data relative to an organization's 1996 sales may be accurate, but they are not dependable when making today's operating decisions. Integrity confirms trustworthiness by directly associating a professional use of data with results; for example, was the data appropriately gathered and are the results reported without bias? In the language of mathematics, accuracy, dependability, and integrity are known as absolute words; that is, words that cannot be modified. For example, a result cannot be "almost accurate" as this expression has no value; an answer is either accurate or inaccurate.

Invent a business scenario about responsible quantitative analysis. Explain what could result if data are not accurate or dependable or do not have integrity. You must include quantitative examples to support your claims.