Editing 1950: Chicken Pox and Name Statistics

Jump to: navigation, search

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.
Latest revision Your text
Line 16: Line 16:
 
The second, seemingly unrelated graph, charts the popularity of certain names over time, in the US. It's normal and expected for certain names to rise and fall in popularity over time, which means that the number of people with those names ends up clustered by age. The names "Sarah" and "Brian" have gone from being highly popular to relatively uncommon for new babies, meaning that people with those names are much likelier to be older. Names like "Logan", "Brooklyn", "Jaxon" and "Harper" went from being virtually unused to having a spurt of popularity, meaning that (as of 2018) people with those names are much more likely to be under the age of 15 than over it.
 
The second, seemingly unrelated graph, charts the popularity of certain names over time, in the US. It's normal and expected for certain names to rise and fall in popularity over time, which means that the number of people with those names ends up clustered by age. The names "Sarah" and "Brian" have gone from being highly popular to relatively uncommon for new babies, meaning that people with those names are much likelier to be older. Names like "Logan", "Brooklyn", "Jaxon" and "Harper" went from being virtually unused to having a spurt of popularity, meaning that (as of 2018) people with those names are much more likely to be under the age of 15 than over it.
  
βˆ’
The final panel points out that these trends, taken together, generate the interesting effect that you can, in some cases, estimate the odds of someone having had chicken pox, based solely on their first name. Having a name like "Brian" or "Sarah" raises the odds that you're over 30, which raises the odds that you had chicken pox. People named "Harper" or "Jaxon" are almost certainly young enough to have grown up with the vaccine in broad use.  These time-based trends predict both the odds of a person having had the illness personally, and the odds that they grew up in a time when infections were common and generally expected.  
+
The final panel points out that these trends, taken together, generate the interested effect that you can, in some cases, estimate the odds of someone having had chicken pox, based solely on their first name. Having a name like "Brian" or "Sarah" raises the odds that you're over 30, which raises the odds that you had chicken pox. People named "Harper" or "Jaxon" are almost certainly young enough to have grown up with the vaccine in broad use.  These time-based trends predict both the odds of a person having had the illness personally, and the odds that they grew up in a time when infections were common and generally expected.  
  
 
The cartoon demonstrates the correlative fallacy, i.e. what can go wrong if one attempts to draw conclusions based on a random comparison of two variables, as described by the famous saying: "{{rw|Correlation_does_not_imply_causation|Correlation does not imply causation}}". In this case, there's a real correlation between names and the incidence of a particular disease. A superficial reading could suggest that either certain names make people prone to the disease, or that the disease, in some way, impacts a person's name. The real cause of this correlation is simply that certain trends just happen to coincide, causing them to statistically correlate without either variable having a real causal affect on the other.  
 
The cartoon demonstrates the correlative fallacy, i.e. what can go wrong if one attempts to draw conclusions based on a random comparison of two variables, as described by the famous saying: "{{rw|Correlation_does_not_imply_causation|Correlation does not imply causation}}". In this case, there's a real correlation between names and the incidence of a particular disease. A superficial reading could suggest that either certain names make people prone to the disease, or that the disease, in some way, impacts a person's name. The real cause of this correlation is simply that certain trends just happen to coincide, causing them to statistically correlate without either variable having a real causal affect on the other.  

Please note that all contributions to explain xkcd may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see explain xkcd:Copyrights for details). Do not submit copyrighted work without permission!

To protect the wiki against automated edit spam, we kindly ask you to solve the following CAPTCHA:

Cancel | Editing help (opens in new window)