# A reviewer wants me to perform a test to *exclude* the presence of a trend. How?

I have an experiment with N=34 measured points. For each point I measure a variable $x$ and a variable $y$. In my paper I reported the average and the standard deviation of $x_i -y_i$ (where $i$ is the sample index). I've qualitatively plotted the scatter plot $x-y$ vs $x$ and I've stated that I do not see any specific trend for $x-y$ vs $x$. Now the reviewer wants me to perform "a statistical test" (without any other specification) to exclude that any such trend exists. How can I do that?

If I measure the correlation coefficient between $x-y$ and $x$ I do find $r \approx 0.45$ which is statistically significant at p<0.05 with N = 34 points. However I'm pretty sure it's due to 4 points that are a bit outside of the normal distribution tails. If I remove them then $r\approx0.1$! These 4 points are clearly outside the main cloud of points.

So, what kind of test can I use to exclude something? Should I measure the average $r$ of a bootstrapping experiment? Is there any other test?

Edit: I've added the scatter plot in question.

• If you want an advice for your specific case then consider showing here your scatter plot. Jun 11, 2018 at 9:29
• I've added the plot. Anyway, I'd be happy even with the concept in general about how to exclude that something is present, without a clear hypothesis about what might be present to begin with. Jun 11, 2018 at 9:35
• (1) Are you sure $r=0.45$ and not $-0.45$? (2) An alternative impression is that $x-y$ varies linearly with $x$, up to some random error. In that case there are no outlying points--but there is a trend. On the face of it--when there is no underlying theory as a guide--this interpretation is the superior one, because it is substantially simpler: describing a trend requires just one additional parameter (the slope) whereas excluding four points requires four additional parameters. (3) You can't test this with the data, because your four-outlier model was developed by examining the same data.
– whuber
Jun 11, 2018 at 13:42

What is asked by the reviewer is not super clear for me...

What I would do is to run a method for detecting outliers. One solution for that can be to use cook distances, which define how influential points are on a model. The cook distance of a point i is the difference of model output with the point i and without.

• For the pure "correlation between y-x an x" question you can use Spearman's $\rho$ to get a robust correlation measure and test. But I don't think I would have opened this up to reviewer scrutiny. I would have used loess to get a smooth nonparametric estimate between x and y. Also compute mean |x - y|. Jun 11, 2018 at 10:46
• Yeah, but at this time I don't have a model! I'll give spearman's a try! Jun 11, 2018 at 11:40