Pearson and non-zero correlation I have data from a genetic experiment, where we have two lenght measurements from each subject (one for each chromosome). In normal conditions I would expect both variables to be normally distributed with similar mean and sd, and fully independent one from the other.
From a technical point of view, both measures are acquired at the same time, and entried in the dataset as "minor" and "major", but could as well be exchangeable.
I want to check if in my sample diverge from the norm a.k.a. if there is a greater degree of similarity in lenght than expected by chance (i.e. because of hidden consanguineity).
I found a strong correlation on Pearson's. However, the scatterplot suggested me that this finding was at least partially the result of the way I entered the data. I thus simulated on SPSS two continuous  variables with random normal distribution, same mean and sd, and 0 correlation.
If I re-ordered each pair in a "minor" and "major", a r = 0.5 circa pops-up, supporting the fact that I introduced a significant artefact and my baseline is not 0.
It still seems to me that in my original data the correlation is greater than expected even taking in account this artefact, however I am stuck: how to demonstrate it?
I have no rational way to enter the data in another order; should I just randomize each pair and test the randomized order? other solutions?

The pairing between the two variables is paramount when it comes to any kind of investigation of dependence or independence. If your data entry introduced or destroyed patterns, and you cannot go back to see what the true pairing was, the results from your false bivariate data are worthless, arguably fraudulent.
I think that’s the right way to think about it: while your lab equipment measured every univariate value you report, your bivariate data are made up.
