<p><strong>Do we need a special tag [dummy-variables]?</strong></p>

<p>Since and thank to <a href="https://stats.meta.stackexchange.com/a/4857/3277">@amoeba's answer</a> (which was well accepted, and I upvoted it too) we've acquired tag <code>[categorical-encoding]</code> - a separate tag marking topics on the ways to recode/represent categorical data in numerical data analysis.</p>

<p>One of the most well-known, ubiquitous way of "categorical encoding" is <strong>dummy variables</strong> (aka <em>indicator</em> variables, aka <em>one-hot</em> variables) - a set of binary variables - each one corresponding to one category - where 1 stands for "this category" and 0 stands for "not this category".</p>

<p>In their point 1 @amoeba thought we don't need a separate tag for dummy variables (<code>I don't think we should have it as a separate tag</code>) as soon as we have <code>[categorical-encoding]</code>. But I <strong>always believed we ought to have</strong> <code>[dummy-variables</code>] along with <code>[categorical-encoding]</code>. I don't think it is deadly critical, only that we'd rather have it than not, as it looks to me. It will be useful.</p>

<p>Reasons to have <code>[dummy-variables</code>]:</p>

<ul>
<li>Dummy-variables are most often used and well-known type of categorical coding. A person may even not be aware of different ways of categorical coding but he is likely to be familiar with "dummy" or "one-hot" terms.</li>
<li>It is used far beyond the domain of representing of a predictor (factor) in ANOVA-like analyses; i.e. far beyond the theme of "contrasts". Almost every kind of data analysis which can manage categorical data will - internally or explicitly - encode it as the binary dummies. Thus, <code>[dummy-variables</code>] makes interest in threads discussing many <em>algorithms</em>, not just threads on categorical analysis.</li>
<li>In "contrast" tasks (categorical predictor) we often consider k-1 set of dummy variables, not full k-variable set (k is the number of categories). It is an important (for parameter interpretation) issue which one is to drop in ANOVA context. But for analyses where we may use all k variables (clustering etc.) that question ("k or k-1 and which to drop?") doesn't belong to. Some users who think of <code>[categorical-encoding]</code> as contaminated with the question will be relieved to find tag <code>[dummy-variables</code>] which isn't necessarily contaminated and may use it calmly, say, in the context of cluster analysis. </li>
<li>Sometimes the input is already a set of dummy variables and the task or the text contains just no word "categorical" anywhere. Therefore the issue that the set of numeric variables potentially represent one nominal variable is not apparent or important at all.</li>
</ul>

<p><strong>Note</strong>: if people decide that we create the tag, but they will oppress "dummy variables" (which term is OK for me), we could name that tag <code>[indicator-variables]</code> or <code>[one-hot]</code> instead.</p>
