Wilcoxon signed rank test for paired, dependent observations I have paired data, but repeated observations, which make the data to be paired with dependent observations. I read Wilcoxon signed rank test assumes the observations are independent. 
Which test can I use if the data contains dependent observations, repeated by groups of 5? This is, the first data points are from a repeated observation, the second 5 from another repeated observation, and so on. Is Wilcoxon ok for this kind of data anyway? 
For example, in the data below, where the same dataset was processed with two different programming languages, having dependent data but not independent observations. My confusion is, Wilcoxon seems to be correct to use here, only if my data was not replicated. Am I right? Which test should I use with dependent samples and dependent observations? 

 A: You can think of your data as blocked, or being a two-factor experiment where one factor is a nuisance.  These are repeated measures, but they are not paired because the blocks aren't of size $2$.  Taking, for example, the data that come from the java code run on a given dataset of 50,000, you have $5$ repeated measures.  These are matched to another $5$ repeated measures from the Python code, in the sense that the codes were run on the same dataset.  A matched pair would be $1$ datum that corresponds to $1$ datum.  You have $5$ data that correspond to $5$ data.  Two matched sets of size $5$ each aren't a 'pair'.  
In theory, you could use a random effect to account for the non-independence of the data.  However, with just three datasets, this is pretty sketchy.  In your case, I would control for the non-independence with a fixed factor.  In other words, you have two factors in your experiment, one of which (dataset) you don't really care about and only want to control for to account for the non-independence that would otherwise occur.  
The question of whether you should make parametric assumptions (like normality) is well addressed in this great CV thread: How to choose between t-test or non-parametric test e.g. Wilcoxon in small samples.  If you wanted to use a nonparametric approach, you could use an ordinal logistic regression (cf., What is the non-parametric equivalent of a two-way ANOVA that can include interactions?).  If you wanted the parametric equivalent, you could run a standard two-way ANOVA.  The ordinal model is probably safer, but it won't matter with your data either way, since the effect is so large.  

On a different note, let me address some of the misunderstandings in the question.  There are two tests that are sometimes called 'the Wilcoxon test':  the Wilcoxon rank sum test, and the Wilcoxon signed rank test.  The former is for two independent samples, and is also called the Mann-Whitney U-test.  The latter is for two dependent samples.  Neither requires that your data are normally distributed.  If your data were paired (e.g., if you ran each dataset only once with each software), then you could use the Wilcoxon signed rank test.  The complication here is that you don't have paired data.  
A: There's no need for any hypothesis testing here. You can see that there's a significant magnitude difference between the Java and Python performance.
A: Here, I assume you're interested in the difference between Java and Python in terms of execution time. I think you should consider paired t-test for each block (data size).


*

*You have paired data (your sample with Java and the same sample with Python)

*Each pair is correlated because you use the same machine to do the test (I assume it's the same machine)

*The execution time can certainly be approximated by normal 

*Your data is blocked by the data size (which is a covariate)


If you want to include all data into a single model, you'll need to use ANCOVA one-way repeated measure.
PS: It's not essential to do statistical testing here because you know the results will be significant and the p-value will be very small.
EDIT @gung doesn't believe your data is paired even you say so. If you don't believe the data is paired, independent t-test and ANCOVA will be good.
