Editing 2893: Sphere Tastiness

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* according to astronaut John Young, who visited the Moon's surface during the Apollo 16 mission, [https://phys.org/news/2006-02-mysterious-moondust.html "moondust doesn't taste half bad"]. (Although other Apollo astronauts likened its smell and taste to burnt gunpowder, so make of that what you will.)
 
* according to astronaut John Young, who visited the Moon's surface during the Apollo 16 mission, [https://phys.org/news/2006-02-mysterious-moondust.html "moondust doesn't taste half bad"]. (Although other Apollo astronauts likened its smell and taste to burnt gunpowder, so make of that what you will.)
  
The title text points out that {{w|baseball (ball)|baseballs}} seem to refute this theory since they're not usually thought of as tasty, but they're between the sizes of grapes and melons, which would place them in the bottom left of the graph, way off the fit line. Baseballs are typically made of a combination of a rubber or cork center wrapped in yarn, and covered by either horsehide, cowhide or synthetic leather. In point of fact, there are many, many common round objects that completely fail to conform to this graph, but rather than acknowledge that this analysis is fatally flawed, Randall uses {{w|special pleading}} to justify its exclusion from the graph, suggesting that the problem is that we lack "the right seasonings". While seasonings can improve the taste of foods, it's implausible that the inedible components of baseballs would be rendered "tasty" with any conceivable combination of seasonings. Even if they could, there's no evidence that such would give them the proper level of 'tastiness' to conform to the graph. This argument lampoons the use of "cherry picking" and motivated reasoning, in which researchers include only data points which fit their hypothesis and make up reasons to exclude those which don't. This is obviously very poor science, but less exaggerated versions are all too common in scientific studies.  
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The title text points out that {{w|baseball (ball)|baseballs}} seem to refute this theory since they're not usually thought of as tasty, but they're between the sizes of grapes and melons, which would place them in the bottom left of the graph, way off the fit line. Baseballs are typically made of a combination of a rubber or cork center wrapped in yarn, and covered by either horsehide, cowhide or synthetic leather. In point of fact, there are many, many common round objects that completely fail to conform to this graph, but rather than acknowledge that this analysis is fatally flawed, Randall suggests that the problem is that we lack "the right seasonings". While seasonings can improve the taste of foods, it's implausible that the inedible components of baseballs would be rendered "tasty" with any conceivable combination of seasonings. This argument lampoons the use of "cherry picking" and motivated reasoning, in which researchers include only data points which fit their hypothesis and make up reasons to exclude those which don't. This is obviously very poor science, but less exaggerated versions are all too common in scientific studies.  
  
 
The comic refers to this plot as research. This is an exaggeration, since two clusters of paired points are rarely considered sufficient for research purposes. But plotting a justifiably sufficient quantity of data points on a logarithmic plot, and then drawing a line through them, is a common way to visualize an actual exponential relationship more comprehensibly. An example of that is the {{w|Gutenberg–Richter law}} where the magnitude of earthquakes (an intrinsically logarithmic scale) in a particular region is plotted together with the frequency of occurrence, typically resulting in a statistically significant straight line.
 
The comic refers to this plot as research. This is an exaggeration, since two clusters of paired points are rarely considered sufficient for research purposes. But plotting a justifiably sufficient quantity of data points on a logarithmic plot, and then drawing a line through them, is a common way to visualize an actual exponential relationship more comprehensibly. An example of that is the {{w|Gutenberg–Richter law}} where the magnitude of earthquakes (an intrinsically logarithmic scale) in a particular region is plotted together with the frequency of occurrence, typically resulting in a statistically significant straight line.

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