<story><title>Is watching the 1984 Ghostbusters movie killing people?</title><url>https://www.covid-datascience.com/post/is-watching-the-1984-ghostbusters-movie-killing-people-a-statistician-s-perspective</url></story><parent_chain><item><author>jkhdigital</author><text>Statistics are meaningless without a rigorously examined causal model of the phenomenon under investigation. In my experience of statistics education, the art of crafting causal theories was scarcely addressed.</text></item><item><author>xoa</author><text>This reminds me of the sort of light poking of common &amp;quot;correlation is not the same as causation&amp;quot; and &amp;quot;beware of confounding factors&amp;quot; statistical failures behind the Church of the Flying Spaghetti Monster &amp;quot;clearly reduction in pirates has caused global warming!&amp;quot; [0]. But it&amp;#x27;s a major in modern public discourse, and one for once that I&amp;#x27;m quite willing to lay heavily at the feet of the public education system. Easily one of the most valuable classes I took in my entire time K-12 was AP Probability &amp;amp; Stats as a sophomore, but that was an entirely optional class with restricted openings anyway (a single teacher in a school of 1200+) which the vast majority never took even where it was offered. Yet interpreting the deluge of data in the modern world requires some level of being able to reason about things probabilisticly and have a sense of what actually goes into a measure of &amp;quot;significance&amp;quot;, null hypothesis and the danger of result-driven analysis finding links that don&amp;#x27;t exist and&amp;#x2F;or cannot possibly be causitive, how sampling a population works and what the error bars look like at different sizes, random vs biased distributions, the underlying distributions, confounding factors etc. I really wish in general kids got started on some light probability thinking as early as possible, in elementary school even, without any real math (let alone calc and such) yet, but just some initial stuff to start to illustrate the mindset. Lots of very fun games and hands-on exercises with dice and so on use or could be made to use important aspects of probability and its misuse.&lt;p&gt;At any rate, I&amp;#x27;m certainly not an expert. But there seems to be some missing BS filter where people can recognize something as silly if the example is silly enough but not in the exact same logic fail for something that seems &amp;quot;more reasonable somehow&amp;quot;.&lt;p&gt;----&lt;p&gt;0: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.spaghettimonster.org&amp;#x2F;wp-content&amp;#x2F;uploads&amp;#x2F;2007&amp;#x2F;10&amp;#x2F;hq-graphcopy2_800.jpg&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.spaghettimonster.org&amp;#x2F;wp-content&amp;#x2F;uploads&amp;#x2F;2007&amp;#x2F;10&amp;#x2F;...&lt;/a&gt;</text></item></parent_chain><comment><author>photochemsyn</author><text>This is very true.  Systematic bias in observational data collection (astronomy etc.) as well as systematic bias in experimental data collection (particle physics etc.) isn&amp;#x27;t accounted for in statistical analysis of that data.&lt;p&gt;A classic example I recall is a Feynmann story, where a group of researchers were getting very statistically sound and repeatable results of very unusual and unexplained particle track behavior in a cloud chamber.  Feynmann looked at the data and said &amp;quot;you probably have a tiny piece of metal in the cloud chamber somewhere&amp;quot; and that turned out to be the explanation.&lt;p&gt;Similar examples in the social sciences include systematic bias in the preparation and administration of IQ tests to different groups of people (see Charles Murray&amp;#x27;s &amp;#x27;Bell Curve&amp;#x27; vs. Stephen J. Gould&amp;#x27;s &amp;#x27;The Mismeasure of Man&amp;#x27;).&lt;p&gt;Hundreds of other examples can be found across all scientific disciplines, unfortunately.  To quote the smartest PI I ever worked for &amp;quot;There&amp;#x27;s a lot of BS in statistical analysis&amp;quot;.</text></comment>
<story><title>Is watching the 1984 Ghostbusters movie killing people?</title><url>https://www.covid-datascience.com/post/is-watching-the-1984-ghostbusters-movie-killing-people-a-statistician-s-perspective</url></story><parent_chain><item><author>jkhdigital</author><text>Statistics are meaningless without a rigorously examined causal model of the phenomenon under investigation. In my experience of statistics education, the art of crafting causal theories was scarcely addressed.</text></item><item><author>xoa</author><text>This reminds me of the sort of light poking of common &amp;quot;correlation is not the same as causation&amp;quot; and &amp;quot;beware of confounding factors&amp;quot; statistical failures behind the Church of the Flying Spaghetti Monster &amp;quot;clearly reduction in pirates has caused global warming!&amp;quot; [0]. But it&amp;#x27;s a major in modern public discourse, and one for once that I&amp;#x27;m quite willing to lay heavily at the feet of the public education system. Easily one of the most valuable classes I took in my entire time K-12 was AP Probability &amp;amp; Stats as a sophomore, but that was an entirely optional class with restricted openings anyway (a single teacher in a school of 1200+) which the vast majority never took even where it was offered. Yet interpreting the deluge of data in the modern world requires some level of being able to reason about things probabilisticly and have a sense of what actually goes into a measure of &amp;quot;significance&amp;quot;, null hypothesis and the danger of result-driven analysis finding links that don&amp;#x27;t exist and&amp;#x2F;or cannot possibly be causitive, how sampling a population works and what the error bars look like at different sizes, random vs biased distributions, the underlying distributions, confounding factors etc. I really wish in general kids got started on some light probability thinking as early as possible, in elementary school even, without any real math (let alone calc and such) yet, but just some initial stuff to start to illustrate the mindset. Lots of very fun games and hands-on exercises with dice and so on use or could be made to use important aspects of probability and its misuse.&lt;p&gt;At any rate, I&amp;#x27;m certainly not an expert. But there seems to be some missing BS filter where people can recognize something as silly if the example is silly enough but not in the exact same logic fail for something that seems &amp;quot;more reasonable somehow&amp;quot;.&lt;p&gt;----&lt;p&gt;0: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.spaghettimonster.org&amp;#x2F;wp-content&amp;#x2F;uploads&amp;#x2F;2007&amp;#x2F;10&amp;#x2F;hq-graphcopy2_800.jpg&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.spaghettimonster.org&amp;#x2F;wp-content&amp;#x2F;uploads&amp;#x2F;2007&amp;#x2F;10&amp;#x2F;...&lt;/a&gt;</text></item></parent_chain><comment><author>calibas</author><text>I don&amp;#x27;t get this attitude, if things are correlated, it should at least make a scientist wonder why. It certainly could be random chance, but correlation can also lead to establishing a causal model or discovering a third variable. If two things keep happening in conjunction, it at least merits further investigation.&lt;p&gt;It seems like there&amp;#x27;s this extreme reaction against people behaving like correlation equals causation, but instead of over-emphasizing correlation, it gets dismissed entirely.</text></comment>