Should dose-response data be normalized before being fit with nonlinear regression?

Prism makes it easy to normalize the data so the values run from 0% to 100%. Simply click Analyze,  choose the Normalize analysis., and define how 0% and 100% are defined.   When fitting a dose-response curve, you can fit either the raw data or normalized data.

Should you normalize the data first?  Some considerations:

• It is not necessary to normalize before fitting dose-response data. In many cases, it is better to show the actual data.
• You can only plot several different dose-response curves on one graph using one axis when they are comparable. If the different experiments measured different variables, normalizing puts them into comparable units. This can be useful.
• Whether or not you choose to normalize your data, you still need to choose how to fit the data. Do you want Prism to find best-fit values for the Top and Bottom plateaus? Or do you want those plateaus to be determined by control data? 
• If you normalize your data, you can choose one of the normalized dose-response equations. These constrain the the curve to run from 0% to 100%. This kind of constraint only makes sense, when 0% and 100% are defined by good control data. If the definitions of 0% and 100% are ambiguous, then so is the definition of "50%", and thus the EC50 is also ambiguous. 
• Just because you chose to normalize your data doesn't mean you must constrain the curve to run from 0 to 100%. You may prefer to have Prism fit those two plateaus.
• If you don't normalize your data, you can use the Constrain tab to fix Top and Bottom to values determined from control experiments. So the decision to constrain Top and Bottom is quite distinct from the decision to normalize your data before fitting.
• It is possible, and can be reasonable, to fix one of those parameters (Top or Bottom) to a constant value but not the other.