<p>Some of you may have heard about the <a href="http://en.wikipedia.org/wiki/Timothy_Gowers#Polymath_Project" rel="nofollow noreferrer">Polymath</a> projects in research mathematics. Polymath is an approach that was conceptiualized by <a href="http://en.wikipedia.org/wiki/Timothy_Gowers" rel="nofollow noreferrer">Timothy Gowers</a> to enable &quot;massively collaborative research&quot; in mathematics. Since the initial conception there are several polymath projects that the math community has started (some of which can be found at this <a href="http://michaelnielsen.org/polymath1/index.php?title=Main_Page--1.1.2.3.4.BlockquoteBlockquote" rel="nofollow noreferrer">wiki</a> for polymath projects.)</p>
<p>Based on my cursory outsider perspective of how these projects have evolved, it seems to me that SE software is not suitable for polymath projects for the following reasons:</p>
<ul>
<li>Multiple attempts to solve a math problem are often tried and a threaded discussion often becomes necessary to make sense of the different proposals.</li>
<li>Voting up/down solution ideas may be counter-productive as what may seem as a dead-end initially can be the 'winner' once the difficulties in the suggested solution are resolved after long back-and-forth discussions.</li>
<li>Votes 'destroys' the threaded nature of comments/answers and in any case there is no downvoting possible for comments.</li>
</ul>
<p>However, I feel that the above disadvantages may not be necessarily hold for applied statistics projects. An applied statistics project typically has the following stages:</p>
<ol>
<li><p>Identify a research question and collect suitable data.</p>
</li>
<li><p>Exploratory data analysis which drives model specification and further analysis.</p>
</li>
<li><p>Choose several competing models that provide alternative answers to the research question.</p>
</li>
<li><p>Perform analysis and select 'best' model</p>
</li>
<li><p>Data visualizations to highlight aspects of the raw data and to highlight model fits etc.</p>
</li>
</ol>
<p>It seems that the SE software can be used for a Polystats project in the following manner. Since each phase of the typical statistics project is modular, we can ask a 'question' for each phase and let the community come up with different answers. For example, suppose that we want to address whether there is global warming (a potentially bad idea for our first polystats project) and that we have temperature data from some external source.</p>
<p>The first question would be:</p>
<ol>
<li><p>Here is the data reg global temperatures for the past xx years. What kinds of plots would help us in specifying a model? An actual plot would be an answer along with a brief explanation why that helps. (perhaps, it suggests a functional form, a data transformation etc).</p>
<p>Once we have collectively exhausted the answers to the above question and reached consensus (e.g., via the two highest voted answers) we lock the question and move on to the next one.</p>
</li>
<li><p>Given what we know from qn 1, what would be some appropriate model specifications.</p>
<p>Again once we have consensus, we lock this question and move on to analysis.</p>
</li>
</ol>
<p>We need to obviously establish some ground rules in order for this process to work well but first things first:</p>
<blockquote>
<p>What do you think about using the SE software to start a &quot;Polystats Project&quot;?</p>
</blockquote>
