Tag Archives: peer review

A Blast from the Past

Sometimes you need a thick skin to be a scientist or scholar. Almost everyone, it seems, has encountered a reviewer who didn’t bother to read what you wrote or badly misunderstood what you said.

In other cases, you realize on reflection that a reviewer’s criticisms, although annoying and even painful at first, are justified in whole or in part. Addressing the reviewer’s criticisms helps you improve your paper or grant. That’s been my experience in most cases.

Sometimes, though, a reviewer just doesn’t like your work. And occasionally they can be pretty nasty about it. Here’s a case that I experienced on submission of the first paper about the Long-Term Evolution Experiment.

{You can click on the image of the review to enlarge it.}

Rev 1 of 1991 LTEE

A few choice lines:

“This paper has merit and no errors, but I do not like it …”

“I feel like a professor giving a poor grade to a good student …”

“The experiment is incomplete and the paper seriously premature …”

“I am upset because continued reliance on statistics and untested models by population geneticists will only hasten the demise of the field.”

“Since the Deans of Science at most universities can only count and not read, I can fully appreciate the reasons for trying to publish this part of the work alone.”

“Molecular biology … should be used whenever possible because molecular biologists control the funding and most of the faculty appointments.”

I’ve occasionally shared this with members of my lab when they get difficult reviews to remind them that it’s not the end of the world or their career, or even the paper that has been scorched.

PS The revised paper was accepted by The American Naturalist. In fact, it won the best-paper award there for the year in which it was published. It has also been cited hundreds of times.

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The Ten Commandments of Statistical Inference

As Handed Down to Lenski by Sir Ronald Fisher

1.  Remember the type II error, for therein is reflected the power if not the glory.

2.  Thou shalt not pseudoreplicate or otherwise worship false degrees of freedom.

3.  Respect the one-tailed test, for it can make thine inferences strong.

4.  Forget not the difference between fixed treatments and random effects.

5.  Thou shalt not commit unplanned comparisons without adjusting the rate of type I error for thy transgressions.

6.  Honor both thy parametric and thy nonparametric methods.

7.  Consider not the probability of a particular set of data, but rather the probability of all those sets as or more extreme than thine own.

8.  Thou shalt confuse neither manipulation and observation, nor causation and correlation.

9.  Thou shalt not presume statistical significance to be of scientific importance.

10.  Thou shalt not be fearful of paying homage to a Statistician or His Holy Book, especially before planning an experiment; neither shalt thou be fearful of ignoring the Word of a Statistician when it is damnable; for thou art alone responsible for thine acceptance or rejection of the hypothesis, be it ever so false or true.

The Golden Rule:  Review unto others as you would have them review unto you.

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Notes:  I wrote this for a graduate course on quantitative methods in ecology and evolutionary biology that I taught in Spring, 1989, at UC-Irvine.  The course focused on experimental design and frequentist methods for drawing inferences.

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