"Dummy variable" versus "indicator variable" for nominal/categorical data "Dummy variable" and "indicator variable" are labels frequently used terms to describe membership in a category with 0/1 coding; usually 0: Not a member of category, 1: Member of category.
On 11/26/2014 a quick search on scholar.google.com (with enclosing quotes) reveals "dummy variable" is used in about 318,000 articles, and "indicator variable" is used in about 112,000 articles. The term "dummy variable" also has a meaning in non-statistical mathematics of "bound variable" which is likely contributing to the greater use of "dummy variable" in indexed articles.
My topically-linked questions:


*

*Are these terms always synonymous (within statistics)?

*Are either of these terms ever acceptably applied to other forms of categorical coding (e.g. effect coding, Helmert coding, etc.)?

*What statistical or disciplinary reasons are there to prefer one term over the other?

 A: @Scortchi has provided a good answer here.  Let me add one small point.  Even using the stricter definition of indicator variable, this can still be associated with (at least) two different coding schemes for categorical data in a regression-type model: viz. reference level coding and level means coding.  With level means coding, you have a categorical variable with $k$ levels that are represented with $k$ indicator variables, but you do not include a vector of $1$s for the intercept (i.e., the intercept is suppressed).  (For a fuller explication, with example model matrices, see my answer here: How can logistic regression have a factorial predictor and no intercept?)  When there is only a single categorical variable, this yields model output in a way that is simple and may be preferred by some people.  (For an example where using this scheme facilitates comparisons of interest, see my answer here: Why do the estimated values from a Best Linear Unbiased Predictor (BLUP) differ from a Best Linear Unbiased Estimator (BLUE)?)
A: I'd say "dummy variable" is a more general way to refer to (one of) the numerical variable(s) that represents (together represent) a categorical predictor; therefore the term applies also to those used in Helmert & effect coding†. That's mainly owing to the general use of "dummy" to mean "stand-in". "Indicator variable" I relate to indicator functions&ddagger;—so those can only be one or zero to indicate having or not having some property; therefore the term applies only to those used in reference-level coding※. Of course some people use "dummy coding" to mean "reference-level coding"; they presumably have a more restricted definition of "dummy variables", or at any rate ought to have.
† And if you don't call those "dummies", what do you call them?
&ddagger; So e.g. the dummy $x_i$ is an indicator variable for when the $i$th person $u_i$ is male (a member of set $M$):
$$
x_i=\boldsymbol{1}_\mathrm{M}(u_i)=\left\{
\begin{array}{l l}
1 & \mathrm{when}\ u_i \in M\\
0 & \mathrm{when}\ u_i \notin M\\
\end{array}\right.$$
where $\boldsymbol{1}_M(\cdot)$ is the indicator function for membership of $M$.
※ Or, as @gung has pointed out, level-means coding.
