Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Well said.  It strikes me that there is much in  the recent assault on p-values that is akin to throwing the baby out with the bath water.  I used to use the analogy of morphine.  Just because morphine can be abused doesn't mean it is always abused.  Now, unfortunately, opioids have become like p-values.

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb

Once upon a time, there was a brilliant scientist who invented a way to tell the difference between signal and noise in messages from data.  His method was not perfect, but it worked well provided the message had been carefully crafted in advance to fit a particular format.

Unfortunately, because the method could be purged of ideas and reduced to a single number, well-meaning journal editors were led to declare, "Our journal will publish only signal, never noise."

Understandably, well-meaning authors, under pressure to publish, were motivated to make their messages look to editors like signal, never noise.

Reality intruded.  Because many messages had not been carefully crafted, their content was mostly noise.

Thus was born the industry of trying to make noise look like signal.  (This last is a lie, of course, or at least an anachronism.  The industry of making noise look like signal was invented by politicians thousands of years before Fisher.  Statisticians merely fortified the hoax by attaching numbers.)

The moral:  Teaching the p-value as an abstract mathematical construct stripped of its context mainly contributes to the noise in the channel of science.  Detecting the signal requires thought.  Study design matters.

George Cobb