Final answer:
Data is inconsistent when the same attribute has different values in different systems, which is a common issue with qualitative data that may use categorical labels like blood type or hair color.
Step-by-step explanation:
Data is considered to be inconsistent when the same attribute has different values in different systems. In the context of qualitative data, this could occur when there are variances in categorical labels that should be uniform across systems. For instance, if one database lists a person's blood type as 'A+' but another lists the same person's blood type as 'AB+', this represents inconsistent data. With qualitative data, attributes are generally classified using words or labels, such as hair color or ethnic group, rather than numerical values.
Data inconsistency can lead to significant issues in data analysis, making it difficult to draw accurate conclusions from the data set. It is essential when handling data to maintain consistency across different systems to ensure the reliability of the data for analysis. This is particularly important for researchers who prefer quantitative data over qualitative data because quantitative data lends itself more easily to mathematical analysis and statistical reliability.