In other words, many combinations of parameter values lead to curves that fit equally well. But when the fit is 'ambiguous', changing other parameters can move the curve so it is near the data again. Changing the value of any parameter will always move the curve further from the data and increase the sum-of-squares.

If you choose (on the Confidence tab) to use the older method of identifying some fits as 'ambiguous', then if a value is preceded by ~, it means the results are 'ambiguous'.

Why is there a ~ symbol in front of some values? If you choose the recommended method, difficult parameters (and their confidence interval) will be marked as 'unstable'. On the confidence tab of the nonlinear regression dialog, you choose how Prism should deal with difficult fits. Should you be fitting a family of datasets together using global nonlinear regression? Why does Prism say 'unstable' rather than reporting a best-fit value?.Should you be constraining a parameter (or several) to constant values? If not, should you be constraining them to have a range of values (only positive numbers, perhaps)?.Does your data actually define all the parameters?.If a value doesn't make any sense, ask your self these questions: What if a parameter value makes no biological or chemical sense? Next, make sure each value makes biological or scientific sense. First make sure you know what units each parameter is expressed in (if there are any units some parameters are unitless constants). Before trying to interpret the rest of the results, first look at the best-fit value of each parameter in your model.

Graphpad prism 8 user guide