Why  nonlinear regression results in Prism 5 can differ from Prism 4.

Prism 5 does not use precisely the same algorithm as did Prism 4, so curve fitting results can be different in rare cases:

• If your fit is labeled "Ambiguous" by Prism 5, you know that some of the parameters are not determined precisely.  Prism 4 presented a full set of results in this case, but the results are not useful when the fit is ambiguous.
• If you chose no weighting, check the sum-of-squares from the two programs. The goal of regression is to minimize that sum of squares, so see which version of Prism found a fit with the smaller sum-of-squares. Prism 5 has a few improvements in the fitting algorithm, so occasionally it can find a better fit than did Prism 4. The differences, if any, are usually trivial.
• If you chose to weight by the Y values (or the Y values squared), Prism 5 handles weighting differently than did Prism 4. Prism 5 weights by the Y value of the curve, while Prism 4 (and earlier releases) weighted by the Y value of the data. The method used by Prism 5 is better, so the results of Prism 5 are more correct. Since the weighting is computed differently, you can't directly compare the weighted sum-of-square values reported by the two versions of Prism.
• When you compare two models, Prism 5 does an extra step. If one of the models is ambiguous, then Prism chooses the other model, without doing the F test or AIC comparison.
• Prism 5 offers more rules for defining initial parameter values. If your equation uses one of these new rules, Prism 4 might not be able to find a reasonable fit until you tweak those initial values. In particular, Prism 5 has smarter rules for fitting sigmoidal log(dose) vs. response curves.
• If you entered data as mean, SD (or SEM) and N, then Prism 4 and 5 fit the data differently. Prism 4 (by default) fits the means and weights by the sample size (N). This is one of the two options on the weighting tab (the other option is to fit means only, ignoring N). Prism 5 is smarter by default (although you can choose to just fit the means and ignore N and SD values). It accounts not only for sample size N but also for the SD (or SEM) values you enter. With Prism 5 (but not Prism 4), you'll get exactly the same results from data entered as mean, SD and N as you would have by entering raw data. Prism 4 only accounts for differences in N, but not SD. The best fit values of the parameters, and thus the appearance of the curve, is the same with Prism 4 and 5. But Prism 5 does a smarter job with standard errors, confidence intervals, and comparisons of models.