Six Sigma Black Belt Certified Practice Exam 2026 – The All-in-One Guide to Master Your Certification!

Attributes data is fundamentally characterized by the fact that it represents counts or categories rather than measurements along a continuum. This type of data categorizes items into distinct groups based on qualitative traits, such as pass/fail, yes/no, or defective/non-defective. Since attributes data focuses on discrete categories or frequencies, it aligns with the classification of discrete data. Each datum in attributes data can only take on certain limited values, meaning it cannot vary continuously as numerical data would.

In contrast, the other options present characteristics that do not apply to attributes data. Continuous data can take on any value within a range, which attributes data cannot. Additionally, the cost of data collection does not define the type of data itself, and while attributes data can sometimes be expensive to collect, that is not a defining characteristic. Lastly, reading from a scale of measurement typically refers to continuous data where precise measurements are taken, which again does not pertain to the nature of attributes data.