Basic Terminology used in Conditional Logistic Regression

I am trying to read up on articles on CLR but they seldom explain what the basic terms actually mean and do not give any simple explanatory examples.
So it is difficult to get past the opening paragraphs.

I am OK with standard Logistic Regression but what does conditional mean here?
What are strata (clusters)?
What is a stratum indicator variable?
What are matched sets?
How do you get results and how accurate are they?

A simple example of each would help greatly.


Less is more. Stay pure. Stay poor.
The difference between LR and CLR, is that the latter is used when you have matched observations. Typical application is when you have control observations matched to known cases. So in the modeling you need to state the matching variable or if observations were matched by strata. That is the difference. However, the one thing to note is that the prevalence can be artificial if you matched cases to controls since the target outcome variable will be perfectly balanced.
Thank you.

What are strata?
Are matched cases not always matched to controls?

Can you give a simple example of the various inputs needed?
Can you help with explaining the other terms I have asked about?
Unless the terminology is explained I cannot get to first base at present.