Two terms that used to define an index’s weight/quality are: selectivity and density. I have come across these terms many times and assumed their meaning which was mostly right. To end those assumptions and have a proper explanation, see the example below:
In a company with 1,000 employees, an index on Date of Birth would likely be highly selective—meaning that a query for employees born on a given day should, statistically, never return more than an average of 3 or 4 employees per day. In SQL Server terminology, this index would have a density of .003% (or 3 out of 1,000), which in turn, would translate to an index selectivity of .997 (index density and selectivity are inversely related or proportional.) Essentially, this index would be beneficial in any query used against the birth date of employees, as the data within this index is ‘selective’ or capable of discriminating against different types of results. Within SQL Server, the more selective an index, the greater the chance that it will get used, and the more efficient it will be at returning results in a performant manner.
Full credit for example goes to an excerpt from a whitepaper by Michael K. Campbell