What are 3 easy, real-world examples for high, medium and low correlation? I understand what correlation means on paper (as in: given high, positive correlation, then, if A is high, B is likely to be high, too). However, the classification into "high", "medium" and "low" correlation seems rather vague to me. I'm not quite sure what to make of it. 
Could you give me intuitive, real-world examples for each level of correlation? 
I'd like to understand it along the lines of "X and Y have a .4 correlation. This means knowing X to predict Y is roughly as useful as knowing today's ice cream sales when predicting the number of people drowned today" [this is completely made up]
 A: So, your understanding is essentially right.  So simply apply your understanding in the real world to something measurable.  For example, what is something that, when larger measurements are obtained on one variable are accompanied by larger measurements on another variable?  Do you think weight and height would be highly correlated?  I think so as taller people tend to weigh more since they tend to have larger body mass.  Can you think of others?  What about a negative correlation (low)?  In this instance an increase in the values of one variable is associated with a decrease in the values of another.  I think daily expenditures on a credit card are almost perfectly negatively correlated with the amount of money in a person's bank account (as expenditures increase, the amount of money in the bank decreases).  Something that has a zero correlation is right in the middle.  So for example, measurements of tides are often described as having zero correlation with time in a 24 hour period [since as time increases for part of the day, the water level rises (i.e. high-tide) and as the day continues after high-tide the water levels sink (i.e. low-tide)].  
The first example is likely something with high correlation, the second with very low correlation, and the last something with no correlation.
You might find the following resource helpful too:  https://www.dummies.com/education/math/statistics/how-to-interpret-a-correlation-coefficient-r/
A: 
...the classification into "high", "medium" and "low" correlation seems rather vague to me.

You are right - the notion of correlation as "high", "medium" or "low" is a fuzzy concept.  Vagueness of the categorisation is an inherent aspect of what happens when you impose discrete categories over a continuum to create a concept of this kind.  It involves a loss of information relative to the continuous scale, and the boundaries of the new discrete categorisation are "fuzzy" (i.e., vague).
