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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Accurate Prediction of Electoral Outcomes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.02664/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1704.02664/feed.xml" rel="self" type="application/rss+xml" /><item><title>Accurate Prediction of Electoral Outcomes — mentioned by lalaithion</title><link>https://news.ycombinator.com/item?id=18704642</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.02664:18704642</guid><pubDate>Tue, 18 Dec 2018 05:03:59 GMT</pubDate><description>&lt;p&gt;&amp;gt; Practically what this means is that they do not predict a winner or looser but instead report a likelihood Yes, this is good. It means that you can evaluate their accuracy to a greater degree than % of times correct. &amp;gt; Further complicating the issue, these predictions are reported as point estimates They&amp;#x27;ve learned from this, and now show their distribution of results very clearly. However, there&amp;#x27;s nothing mathematically fraught about reporting a prediction as a point estimate. &amp;gt; The problem is that models are not perfect replicas of the real world and are, as a matter of fact, always wrong…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.02664/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.AP</category></item></channel></rss>
