modeling data dimensions as columns - some dimensions are highly variable while others have low variability Please excuse my question if it is a bad one - I am not a DBA...

I would like to model the following data: 

Let's say that I have one column that is the row id as generated by a sequence. Let's also say that I have 3 columns: A, B &amp; C that have low data variability. Let's also say that I have 4 more columns I, II, III, IV with high data variability. 

As a concrete example, lets say that the values of A, B &amp; C can be any number between 1 - 50, and the values of I, II, III &amp; IV can take any textual data, as another example, any number between 1 - 1B.

The question:
Should I model a single table with columns: row_id, A, B, C, I, II, III, IV. Or should I model two tables the first to contain the low variability data columns: row_id, A, B, C and the second to contain the rest of the columns while referencing the row_id of the first table: row_id, first_table_row_id, I, II, III, IV.

I am using postgresql. What answer best fits that database? What answer is the generic best?

Also, there might not be a clear cut answer, if so, what are the pros and cons to each modeling scheme?
I would suggest creating a separate dimension for your high variability columns. This is a Type 4 mini dimension according to Kimball methodology. 

http://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/dimensional-modeling-techniques/type-4-mini-dimension/

Hope this helps.