Final answer:
The correct explanation for why much data collected is redundant or irrelevant is Data Overload (B), which refers to being overwhelmed by excessive data, making it difficult to focus on relevant information.
Step-by-step explanation:
Understanding Data Collection Issues
When analyzing why much data collected is redundant or irrelevant, the correct explanation among the given options is Data Overload (B). Data Overload refers to the issue where individuals or systems are overwhelmed by the amount of data they have to process and analyze. This excessive data can lead to difficulty in focusing on relevant data points and can result in the collection of redundant or irrelevant information.
While sampling bias and response bias are important concepts in data collection, they particularly address the representativeness of a sample or the accuracy of participants' responses, rather than the volume of data itself. Information overload is very similar to Data Overload and also contributes to the same consequence of collecting data that may not be necessary or relevant to the study's objective.
Therefore, in the context of too much data being collected without discerning importance, Data Overload or Information Overload are the phenomena that best explain why excessive, redundant, or irrelevant data is gathered.