"Distributions", also called "generalized functions" are essentially functionals, that is, things that map functions to numbers.
We can think of regular functions as being "distributions" in this way: the function f(x) maps any function g(x) to the number [itex]\int_{-\infty}^\infty f(x)g(x)dx[/itex].
Another example of a distribution is the "Dirac delta function" (which is what started the investigation into distributions because it is NOT a function). The Dirac delta function, that P.A.M. Dirac used in quantum physics papers as the "exact position" operator, was defined in a very rough way as a function that was 0 for every x except x= 0 and was infinite at x= 0 in such a way that the integral [itex]\int_{-\infty}^\infty \delta(x) dx= 1[/itex].
Of course, there is no such "function" but calculations done with it worked! It could be formalized by thinking of it as the functional that, to every function f(x), assigned the value f(0). Then, in a rough sense, [itex]\int_{-\infty}^\infty \delta(x)f(x)dx= f(0)[/itex].
One useful property that distributions have that functions don't is that every distribution is infinitely differentiable. Since we can think of functions as being as subset of distributions, a function may not have a function as its derivative but it can have a distribution, that is not a function, as its derivative. For example, the function f(x)= 0 if x< 0, f(x)= 1 if [itex]x\ge 0[/itex] has the Dirac delta "function" as its derivative. It is not differentiable, at x= 0, in the sense of functions, but it is differentiable "in the distributional sense".