Why is this reviewer's comment funny: "Unless my statistics is failing me, a less than 1.0 SD is not significant"? I found this phrase on one of the web sites that publish reviewer's quotes (they suppose to be funny). One of the reviewers told to the authors of paper:
First, unless my statistics is failing me, a less than 1.0 SD is not significant. 
I do not know the paper, but I know tags under this comment:
#you were right about one thing #yes your statistics is failing you #submission

What does it mean? As for me, reviewer told that the result is in less than 1 SD distance from the mean, that means that p-value is quite big and is not significant, so the reviewer was right, but the hash tag states the opposite.
Could somebody clarify what happens here, why the reviewer's comment is so short (as for me, there is no enough information) and what does it mean?
UPD: To whom who thinks that it is not clear what I am asking about. I am asking "Is there something that I miss in the information?" It happened with me once, when I heard "p-value of 0.05 means that you are wrong in approx 30% of the cases". It made no sense for me at first sight, but with the help of the community I understood an important concept. Unfortunately, it did not happen for this case.
 A: The reviewer is apparently trying to use the standard deviation as some sort of ad-hoc statistical test, but this doesn't work. The reviewer's snarky "unless my statistics is failing me" comment is therefore funny (or maddening, if this gets your paper rejected).
Specifically, standard deviation tells us how spread out the values are around the mean value. However, in most hypothesis-testing situations, we are interested in determining the means of each group and whether these means differ. To do this, we need to determine how accurately we know the mean of each group, and the relevant statistic here is the standard error of the mean, not the standard deviation. (These are related, in that $\textrm{se}_\textrm{mean} = \frac{s}{\sqrt{n}}$, where $s$ is the sample standard deviation and $n$ is the number of samples). 
In a $t$-test, the denominator is often the standard error of the mean (or something like it for two-sample t-tests). 
It's easy simulate data that shows it is possible to find significant differences between groups that have a standard deviation of at least one. For example, 
a = rnorm(100, 0, 1)  # Draws 100 random points from N(0,1)
b = rnorm(100, 1, 1)  # Draws 100 random points from N(1,1)
t.test(a, b)
#   Welch Two Sample t-test
#
#   data:  a and b
#   t = -8.0116, df = 190.746, p-value = 1.097e-13
#   alternative hypothesis: true difference in means is not equal to 0
#   95 percent confidence interval:
#     -1.4188010 -0.8581969
#   sample estimates:
#     mean of x  mean of y 
#     -0.1154137  1.0230852 

Similarly, you can imagine a scenario in which the data where each standard error of the mean is much larger than one, yet a $t$-test finds a significant difference. For example, suppose the means were -100 and 100: with enough data, we'd be able to tell them apart even if the standard deviation were large (e.g., $\sigma=30$ for $n=100$)
In summary, the reviewer has totally failed at statistics, which makes his/her snarky comment funny.
A: It's unclear without more information. Here's an example where the standard deviation is less than 1.0, but the test is significant:
# draw 100 samples from two random Normal distributions with small standard deviations,
#  with different means:
x  <- rnorm(100, 1, .5)
x2 <- rnorm(100, 3, .5)

# note the standard deviation of the difference in means that will be tested:
sd(x-x2)

# here's the significant result:
t.test(x, x2)

Does that help?
A: Most likely the reviewer means that your parameter is closer than one standard deviation to the null hypothesis value. For instance, your regression slope coefficient is 0.5 while its standard deviation is 1. I emphasized to assume that the reviewer is using the correct standard deviation, i.e. adjusted to the sample size blah-blah. In this case if you're testing whether there is a slope in the regression, you compare 0.5 to 0, and observe that it's closer than the standard deviation.
For instance, consider gamma distribution with parameters $\alpha=0.1$ and $\beta=10000$, its mean is 1000 and $\sigma=3162$. If you test the distance between the mean and 1e-10 then the one tailed test would give you 5% significance. It's very skewed distribution.
Can you construct an example where p-value is significant while the distance is less than the standard deviation? Yes, of course. However, in most regressions this simple heuristic based on the standard deviations works fine. Moreover, to determine significance, you usually have to assume the probability distribution. While a simple comparison to the standard deviation does not require any assumptions on distributions. Therefore, I'd side with your reviewer in this case. Unless you have reasons to believe that she can't calculate the appropriate standard deviation of the parameter, which is unlikely (why would a reviewer be incompetent?).
Also, in applied work folks (like myself) speak in terms of standard deviations all the time. For instance, when talking about the accuracy of an instrument or economic significance of the coefficient. It's a very good measure of the dispersion.
