Final answer:
Relatively low precision categorical data lacks accuracy and consistency, while relatively high precision categorical data is more specific and consistent.
Step-by-step explanation:
Relatively low precision categorical data refers to data that is not very specific or accurate.
An example of this could be a survey question that asks people to rate their satisfaction on a scale of 1 to 10.
The responses may vary widely and lack consistency, leading to low precision.
On the other hand, relatively high precision categorical data is more specific and consistent.
For example, a survey question that asks people whether they prefer red, blue, or green would likely result in more precise data as there are limited options.