Why does absolute error not decrease at the inflection points with trapezoidal rule used on standard normal distribution curve? If I use the trapezoidal rule to work out the area under a normal distribution curve from $x=0$ to $x=b$ where $b>0$, surely I expect the error value to begin to decrease past the inflection point (i.e. x = $+\sigma$) as the concavity has begun to change here and therefore trapezii have gone from overestimating on the concave down part of the curve (from x=0 to x=$\sigma$) to underestimating on the concave up part (x > $\sigma$).
I cannot understand why the absolute value of percentage error (and absolute error) begins to decrease a little after this point. I have attached a graph from a simulation I ran in C++, I also checked it using a tedious method in Excel (so I am certain no error in my coding).
Thank you to anyone that can shed light on this bizarre result! 
Edit: For this graph $\sigma = 1$ and the number of trapezii used is fixed.
Graph showing percentage error in using trapezoidal rule to evaluate area under Standard Normal Distribution curve from 0 to upper limit b.

I have a reponse to my own questions finally. Firstly I should clarify that $\sigma = 1$ in my graph, as I used the standard normal distirbution.
The reason, for a fixed number of strips, the absolute error does not begin to decrease immediately at x=1 can be explained by thinking about the following example:
Let's take n=110 strips for ease and say I use this to approximate the area from 0 to 1, then I have 110 strips in the underestimating section. Now imagine I use 110 strips again to go from 0 to 1.1, this implies I have only 100 strips from 0 to 1 and 10 strips from 1 to 1.1 now. The fact that I have only 100 strips in the underestimating part now, compared to 110 previously, has a bigger effect on the error than the fact that there are now an extra 10 strips overestimating from 1 to 1.1. Hence the error is actually more negative from 0 to 1.1 than from 0 to 1 for a fixed number of strips.
Not immediately obvious at all, but this explains why the graph doesn't turn at x = 1, but rather a little afterwards.
