  This is a non-technical introduction into theory of contextuality. More
precisely, it presents the basics of a theory of contextuality called
Contextuality-by-Default (CbD). One of the main tenets of CbD is that the
identity of a random variable is determined not only by its content (that which
is measured or responded to) but also by contexts, systematically recorded
conditions under which the variable is observed; and the variables in different
contexts possess no joint distributions. I explain why this principle has no
paradoxical consequences, and why it does not support the holistic "everything
depends on everything else" view. Contextuality is defined as the difference
between two differences: (1) the difference between content-sharing random
variables when taken in isolation, and (2) the difference between the same
random variables when taken within their contexts. Contextuality thus defined
is a special form of context-dependence rather than a synonym for the latter.
The theory applies to any empirical situation describable in terms of random
variables. Deterministic situations are trivially noncontextual in CbD, but
some of them can be described by systems of epistemic random variables, in
which random variability is replaced with epistemic uncertainty.
Mathematically, such systems are treated as if they were ordinary systems of
random variables.
