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Let $\textbf A$ denote the space of symmetric $(n\times n)$ matrices over the field $\mathbb K$, and $\textbf B$ the space of skew-symmetric $(n\times n)$ matrices over the field $\mathbb K$. Then $\dim (\textbf A)=n(n+1)/2$ and $\dim (\textbf B)=n(n-1)/2$.
Short question: is there any short explanation (maybe with combinatorics) why this statement is true?
EDIT: $\dim$ refers to linear spaces.
All square matrices of a given size $n$ constitute a linear space of dimension $n^2$, because to every matrix element corresponds a member of the canonical base, i.e. the set of matrices having a single $1$ and all other elements $0$.
The skew-symmetric matrices have arbitrary elements on one side with respect to the diagonal, and those elements determine the other triangle of the matrix. So they are in number of $(n^2-n)/2=n(n-1)/2$, ($-n$ to remove the diagonal).
For the symmetric matrices the reasoning is the same, but we have to add back the elements on the diagonal: $(n^2-n)/2+n=(n^2+n)/2=n(n+1)/2$.
Here is my two cents:
\begin{eqnarray} M_{n \times n}(\mathbb{R}) & \text{has form} & \begin{pmatrix} *&*&*&*&\cdots \\ *&*&*&*& \\ *&*&*&*& \\ *&*&*&*& \\ \vdots&&&&\ddots \end{pmatrix} \hspace{.5cm} \text{with $n^2$ elements}\\ \\ \\ Skew_{n \times n}(\mathbb{R}) & \text{has form} & \begin{pmatrix} 0&*'&*'&*'&\cdots \\ *&0&*'&*'& \\ *&*&0&*'& \\ *&*&*&0& \\ \vdots&&&&\ddots \end{pmatrix} \end{eqnarray} For this bottom formation the (*)s and (*')s are just operationally inverted forms of the same number, so the array here only takes $\frac{(n^2 - n)}{2}$ elements as an argument to describe it. This appears to be an array geometry question really... I suppose if what I'm saying is true, then I conclude that because $\dim(Skew_{n \times n}(\mathbb{R}) + Sym_{n \times n}(\mathbb{R})) = \dim(M_{n \times n}(\mathbb{R}))$ and $\dim(Skew_{n \times n}(\mathbb{R}))=\frac{n^2-n}{2}$ then we have that \begin{eqnarray} \frac{n^2-n}{2}+\dim(Sym_{n \times n}(\mathbb{R})))=n^2 \end{eqnarray} or \begin{eqnarray} \dim(Sym_{n \times n}(\mathbb{R})))=\frac{n^2+n}{2}. \end{eqnarray}
This is a bit late, but I figured I would chime in since the other answers don't really give the combinatorial intuition asked for in the original question.
In order to specify a skew-symmetric matrix, we need to choose a number $A_{ij}$ for each set $\{i,j\}$ of distinct indices. How many such sets are there? Combinatorics tells us that there are $\left( \begin{array}{@{}c@{}} n \\ 2 \end{array} \right) = \frac{n(n-1)}{2}$ such sets.
Similarly, in order to specify a symmetric matrix, we need to choose a number $A_{ij}$ for each set $\{i,j\}$ of (not necessarily distinct) indices. We can encode such sets by adding a special symbol $n+1$ that indicates a repeated index. Thus, we need to choose a number $A_{ij}$ for each set $\{i,j\}$ of distinct symbols, where now a symbol is either an index ($1 , \ldots , n$), or our special symbol $n+1$ that is used for a repeated index. Combinatorics tells us that there are $\left( \begin{array}{@{}c@{}} n+1 \\ 2 \end{array} \right) = \frac{n(n+1)}{2}$ such sets.
The dimension of symmetric matrices is $\frac{n(n+1)}2$ because they have one basis as the matrices $\{M_{ij}\}_{n \ge i \ge j \ge 1}$, having $1$ at the $(i,j)$ and $(j,i)$ positions and $0$ elsewhere. For skew symmetric matrices, the corresponding basis is $\{M_{ij}\}_{n \ge i > j \ge 1}$ with $1$ at the $(i,j)$ position, $-1$ at the $(j,i)$ position, and $0$ elsewhere.
Note that the diagonal elements of skew symmetric matrices are $0$, hence their dimension is $n$ less than the dimension of normal symmetric matrices.
There have been many very good answers, so this is going to be another perspective of counting. We tackle the case of skew-symmetric first, which yields the condition that for any matrix $A$ with real entries, $a_{ij} = -a_{ij}$, so this means one side of the matrix is completely determined by the other as shown.
$$\begin{matrix} \begin{pmatrix} 0 &*' \\ *& 0 \\ \end{pmatrix} & * \end{matrix}$$
$$\begin{matrix} \begin{pmatrix} 0 &*' & *' \\ *& 0 & *' \\ * & * & 0 \\ \end{pmatrix} & \begin{matrix} *& \\ * & * \end{matrix} \end{matrix}$$
$$\begin{matrix} \begin{pmatrix} 0 &*' & *' &*'\\ *& 0 & *' &*'\\ * & * & 0 &*'\\ * & * &* & 0 \end{pmatrix} & \begin{matrix} * & & \\ *&* & \\ * & * &* \end{matrix} \end{matrix}$$
$$\begin{matrix} \begin{pmatrix} 0&*'&*'&*'&\cdots \\ *&0&*'&*'& \\ *&*&0&*'& \\ *&*&*&0& \\ \vdots&&&&\ddots \end{pmatrix} & \begin{matrix} *& \\ *&*& \\ *&*&*& \\ \vdots&&&&\ddots \end{matrix} \end{matrix}$$
Notice that the triangle keeps getting larger and larger and at each stage and is incremented by $+1$. Since we have $n-1$ entries to consider we add up the number of entries, that is $$1+ 2 + \dots + (n-1).$$
So by Gauss, we have $$\frac{(n-1)(n-1+1)}{2} = \frac{(n-1)n}{2}$$ degree of freedom and that is the dimension we seek.
For the symmetric case we need to add back the $n$ objects in diagonal zeroed out, so $$\frac{(n-1)n}{2} + n = \frac{n^2 + n}{2}$$
It doesn't really help find the exact answers, but here is a little bit of intuition on why the sum of dimensions of the two spaces equals exactly $n^2.$
Denote the subspace of symmetric and skew-symmetric matrices, $W_1$ and $W_2$, respectively (the whole space $V$). Note that for any $A\in K^{n\times n}$, we may write
$$ A=\frac{1}{2}\left(A+A^{t}\right)+\frac{1}{2}\left(A-A^{t}\right) $$ where $\frac{1}{2}\left(A+A^{t}\right)$ is symmetric and $\frac{1}{2}\left(A-A^{t}\right)$ is skew-symmetric. Moreover, noticing $W_1\cap W_2=\{0\}$,
$$ V=W_1\oplus W_2 \text{ and } \dim V=n^2=\dim W_1+ \dim W_2. $$
