Figure 3 shows why assuming either C or n as fixed is not reasonable. The horizontal line is at n = 2.87, the average of 68 Paris slopes. This is a reasonable value only when -6.58 < C < -6.45. When C is outside this range, as it will be often, the resulting simulated combination is very, very improbable. In fact observations in either the first or third quadrants (large n with large C, or small n with small C) are exceedingly unlikely in reality but occur about half the time in uncorrelated simulation.

Another flawed option for remedy suggests itself since the two parameters are obviously so closely related: let one be a function of the other. A linear fit of C = b₁ + b₂ * n, with n being sampled from a normal density, does indeed improve things. But this time the resulting error ratio is 0.51, i.e.: the scatter has been over-corrected, and now is underestimated by almost half. Clearly this nonconservative result is also unacceptable.

To summarize:

1)   Monte Carlo simulation requires more than substituting probability densities for constants.
2)   Getting the Physics right isn't enough - Ignoring the statistical interplay among regression parameters can lead to hopelessly inaccurate results.
3)   All MC simulations can be vulnerable to errors of this kind.   The lessons here apply to any regression model, not just to these data, nor only to crack growth behavior.
4)   Doing the MC simulation right - sampling from the joint density of the model parameters, not from their marginals - is easier than doing it with ad hoc methods whose statistical properties are unknown. (Well, they're not really "unknown."  They're awful.)
5)   Not understanding the nature of the statistical assumptions being made does not mean that they do not exist.  Mother Nature doesn't care if you're paying attention or not, and she will do what she will, regardless of your calculations.