Question

When cleaning/parsing data removes a potentially important attribute, that is referred to as

A: Confirmation bias
B: Automation bias
C: Exclusion bias
D: Observer bias

Answer 1

Exclusion bias is when important data is unintentionally or intentionally left out during the data cleaning/parsing process, potentially affecting the validity of the data analysis.

Step-by-step explanation:

When cleaning/parsing data removes a potentially important attribute, it is referred to as C: Exclusion bias. Exclusion bias occurs when a researcher or data scientist unintentionally or intentionally omits relevant data, which can create a skew in the resulting sample or analysis. This contrasts with confirmation bias, which is the tendency to focus on information that confirms existing beliefs. Other types of cognitive biases include anchoring bias, representative bias, hindsight bias, and the availability heuristic. Each of these biases can impact decision-making and reasoning in different ways, but specifically, exclusion bias is related to the potential error in data preparation that could impact the validity of the data analysis.