unexpected high precision of the Simpson's rule I tried to use the Simpson's rule to calculate the following integral
$$ \pi = 4 \int_0^1 \frac{dx}{1+x^2} .$$
The Matlab code is as follows. I expected a scaling law $error \propto h^4 $
Nlist = 2.^(2:6);
elist = 0 * Nlist;
for s1 = 1 : length(Nlist)
    N = Nlist(s1);
    h = 1/N;
    xlist = 0 : h/2 : 1;
    ylist = 1 ./ (1 + xlist.^2 );

    sum1 =4*h * ( (ylist(1) + ylist(end))*(1/6)...
        + sum(ylist(2:2: end-1))*(4/6)...
        + sum(ylist(3:2 : end-2))* (2/6));

    error = abs(sum1 - pi);
    elist(s1) = error;
end

plot(log2(Nlist), log2(elist),'*')
xlabel('log2(N)')
ylabel('log2(error)')

The figure generated is

Slope is about $-6$. I expected it to be $-4$. The Simpson's rule works more accurately than expected. I am totally baffled. Is it because of some hidden property of the integrant?
 A: Let us consider the problem of computing the integral $$T = \int_a^b f(x)dx $$ using both the composite trapezoidal rule $T_h$ as well as the Simpson rule $S_h$. If $f \in C^{(2k+1)}([a,b],\mathbb{R})$ then the error satisfies an asymptotic error expansion of the form $$T - T_h = \sum_{j=1}^k \alpha_j h^{2j} + O(h^{2k+1}), \quad h \rightarrow 0, \quad h > 0.$$ The exact value of the expansion coefficients are given by
$$\alpha_j = \frac{B_{2j}}{(2j)!}(f^{(2j-1)}(b) - f^{(2j-1)}(a))$$ where $B_{j}$ is the jth Bernoulli number. We need only the first few terms, namely $$B_2 = \frac{1}{6}, \quad B_4 = \frac{1}{30}, \quad B_6 = \frac{1}{42}.$$
In the present case, we have $$f(x) = (1+x^2)^{-1}.$$ It is tedious but straightforward to verify that $$\alpha_1 \not = 0, \quad \alpha_2 = 0, \quad \alpha_3 \not = 0.$$
Hence the error for the trapezoidal rule is $O(h^2)$. Simpson's rule can be obtained from the trapezoidal rule using Richardson extrapolation. Specifically, we have $$S_h = T_h + \frac{T_h - T_{2h}}{3}.$$ Richardson extrapolation eliminates the primary error term from the error expansion and exposes the next term. In this case it is $O(h^6)$ because there never was a term that was $O(h^4)$.
A: The leading order error term for an integration quadrature $Q$ on an interval $[a, b]$ that integrates monomials up to degree $n$ exactly is of the form $C h^{n + 1}(f^{(n)}(b) - f^{(n)}(a))$, where $C$ is a constant independent of $f$ and $h$. This is proven easily as follows.
First consider applying the rule on a small interval $[-h, h]$. Define $E(f) = \int_{-h}^{h}f(x)\,dx - Q(f)$. Note that since integration and $Q$ are linear, $E$ is linear. Expand $f$ as a Taylor series around $0$:
$$f(x) = \sum_{j = 0}^{n}\frac{f^{(j)}(0)}{j!}x^j + \frac{f^{(n + 1)}(0)}{(n + 1)!}x^{n + 1} + \frac{f^{(n + 2)}(\theta(x)x)}{(n + 2)!}x^{n + 2}.$$
Apply $E$ to both sides, noting that terms $E(x^j) = 0$ for $j \leq n$ to get
$$E(f) = \frac{f^{(n + 1)}(0)}{(n + 1)!}E(x^{n + 1}) + E(\frac{f^{(n + 2)}(\theta(x)x)}{(n + 2)!}x^{n + 2}).$$
Note generally that $E(x^j) = O(h^{j + 1})$. Similarly, as long as $f^{(n + 2)}$ is bounded (which happens if $f \in C^{n + 2}([a, b])$), then $E(\frac{f^{(n + 2)}(\theta(x)x)}{(n + 2)!}x^{n + 2}) = O(h^{n + 3})$. So for the purposes of finding the leading error term, we may drop the term $E(\frac{f^{(n + 2)}(\theta(x)x)}{(n + 2)!}x^{n + 2})$. Thus
$$E(f) \approx \frac{f^{(n + 1)}(0)}{(n + 1)!}E(x^{n + 1}) = Cf^{(n + 1)}(0)h^{n + 2}.$$
Now for the error $e(h)$ on the composite method on $[a, b]$ with subintervals of length $2h$, we add the errors on each subinterval $[c_j - h, c_j + h]$ up to get
\begin{align}
e(h) &\approx \sum_{j = 0}^{N - 1}Cf^{(n + 1)}(c_j)(2h)^{n + 2} \\
&= C(2h)^{n + 1}\sum_{j = 0}^{N - 1}f^{(n + 1)}(c_j)(2h) \\
&\approx C(2h)^{n + 1}\int_{a}^{b}f^{(n + 1)}(x)\,dx \\
&= C(2h)^{n + 1}(f^{(n)}(b) - f^{(n)}(a)).
\end{align}
Note that in the third equality we recognized the midpoint rule as an approximation of $\int_{a}^{b}f^{(n + 1)}(x)\,dx$.
In your case, we have Simpson's rule, which has $n = 3$. With your function $f(x) = \frac{1}{1 + x^2}$, $f^{(3)}(1) - f^{(3)}(0) = 0$. The explanation for why your order was $6$ is as follows. Carrying out a similar proof to above on the midpoint rule (say to the first two terms), you can see that the midpoint rule error $e_m(h)$ has an "asymptotic expansion" as
$$e_m(h) = C_1h^2(f'(b) - f'(a)) + C_2h^4(f^{(3)}(b) - f^{(3)}(a)) + \dots.$$
With this in hand, you can carry out the above proof for Simpson's rule further (say to the first two terms. Note that $E(x^j) = 0$ for odd $j$ due to symmetry), and you will see that the asymptotic error is of the form ($C_1$ and $C_2$ here are different than the ones in the midpoint rule)
$$e_s(h) = C_1h^4(f^{(3)}(b) - f^{(3)}(a)) + C_2h^6(f^{(5)}(b) - f^{(5)}(a)) + \dots$$
The exact coefficients $C_j$ in the midpoint expansion can be written in terms of Bernoulli numbers (Euler Maclaurin formula).
