Something for the weekend, sir?: Difference between revisions

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{{a|devil|{{catbox|newsletter draft}}}}Newsletter crib-notes
{{a|devil|{{catbox|newsletter draft}}}}Newsletter crib-notes
More on averagarianism and customer surveys
====In progress====
*[[Working from home]]
*[[Data modernism]]
*[[ABS field guide]]
*[[Org chart]]
*[[System redundancy]]
*[[Legal evolution]]
====More on averagarianism and customer surveys====
Customer surveys are a kind of self-serving averagarianism. To ask online subscribers "how satisfied are you with the quality of the Times’ journalism” on a five point scale from “extremely satisfied” to “extremely dissatisfied” asks the user to construct some sort of ad hoc blended average of the quality of all the writing in the paper, whereas it inevitably varies between departments, between writers, topics, articles, and even days of the week. And that average reflects the priorities and values of the individual readers, who are not the same. Some might buy the times —and therefore judge it —  for its sports coverage, others for the comment, business, politics or cultural coverage, or any combination. The times, we imagine, already knows which subscribers read which articles, so it is not learning anything useful by asking an artificial question, aggregating what are effectively responses to different questions, which users are already answering in the affirmative, anyway, because we should presume they buy the paper for the parts they like, and they like the parts they buy the paper for.  
Customer surveys are a kind of self-serving averagarianism. To ask online subscribers "how satisfied are you with the quality of the Times’ journalism” on a five point scale from “extremely satisfied” to “extremely dissatisfied” asks the user to construct some sort of ad hoc blended average of the quality of all the writing in the paper, whereas it inevitably varies between departments, between writers, topics, articles, and even days of the week. And that average reflects the priorities and values of the individual readers, who are not the same. Some might buy the times —and therefore judge it —  for its sports coverage, others for the comment, business, politics or cultural coverage, or any combination. The times, we imagine, already knows which subscribers read which articles, so it is not learning anything useful by asking an artificial question, aggregating what are effectively responses to different questions, which users are already answering in the affirmative, anyway, because we should presume they buy the paper for the parts they like, and they like the parts they buy the paper for.  


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