Let $X = \set{a, b}$ and $Y = \set{0, 1}$. Define $f: X \to Y$ by $f \equiv 0$.

Consider “selecting” elements of $X$ so to determine $f$. Since $f$ is functional, the program is straightforward. Observe $f$ at $a$ and at $b$, and then any consistent inductor will determine $f$.

So, in some sense, the dataset $((a,0), (b,0))$ is “good” in that “reasonable” learning algorithms (those which are consistent) will determine $f$ from a dataset consistent with $f$. What of more general domains $X$?

Let $X$ and $Y$ be sets. Let $\set{G_n: (X \times Y)^n \to (X \to Y)}_{n \in \N}$ be a family of inductors.

An input design (or experiment design or experimental design) of size $n$ is a sequence of inputs $x_1, \dots , x_n \in X$. The interpretation is that we will observe a dataset $(x_1, y_1)$, $\dots $, $(x_n, y_n)$ consistent with some function and that we will “infer” the function $G((x_i,y_i)_{i = 1}^{n})$.

Some authors define a design as a sequence $x_1, \dots , x_\ell $ and nonzero integers $n_1, \dots , n_{\ell }$ so that $\sum_{i \leq \ell } n_i = n$. The correspondence with our definition is obvious.

A conditional input design (of size $k$) for a dataset $(x_1,y_1)$, $\dots $, $(x_m, y_m)$ is a design $x_{m+1}, \dots , x_{m+k} \in X$. The interpretation is that we will observe a dataset $(x_1, y_1)$, $\dots $, $(x_{m+k}, y_{m+k})$ consistent with some function and that we will “infer” the function $G((x_i,y_i)_{i = 1}^{m+k})$.

For both of these designs, our interpretation of these objects is “experimental.” We call $x \in X$ the experimental conditions and we call $X$ the experimental domain. We think of “choosing” a design and then “running an experiment” or “collecting data.” The experimental conditions are understood to be freely chosen (within the domain $X$). We call an element of the dataset $y_i$ an observed response or yield.

Let $f: X \to Y$ and suppose the dataset is consistent with $f$. Let $\Theta $ be a nonempty set. Suppose there exists $\theta ^\star \in \Theta $ and $g: \Theta \times X \to Y$ so that $g(\theta ^*, x) = f(x)$ for all $x \in X$ (and so denote $f$ by $g_{\theta ^{\star}}: X \to Y$).

In this case we call $\theta $ the parameters or parameter system and we call $\Theta $ the parameter domain (or parameter space). We call the dataset parametrically consistent.

This decomposition $(\theta , x)$ is interpreted as reflecting what is within and without of the experimenter's control. The conditions can be chosen by the experimenters, but the parameters are fixed, or “chosen by nature.” The experimenter “controls” the conditions and nature “controls” the parameters.