Which factor would NOT be considered when assessing data trustworthiness?

When evaluating the trustworthiness of data, several critical factors are typically considered, such as dependability, completeness, and reliability. Each of these factors contributes to determining how much you can trust the data for making informed decisions.

Dependability refers to the consistency and stability of the data over time. It includes how well the data performs in different conditions and records.

Completeness measures whether all necessary data is present and sufficiently detailed for its intended purpose. Missing data can undermine the trustworthiness of the dataset.

Reliability assesses how consistently the data can be counted on to provide accurate and relevant results when used over time.

On the other hand, popularity does not influence the trustworthiness of data. While widely used or cited data can suggest a level of credibility, it does not inherently indicate the quality, accuracy, or reliability of the data itself. Therefore, it’s not a factor that should be considered in assessing data trustworthiness.