I have two large data sets, in fact, one of them is even much larger than the other.
Visually, there doesn't seem to be that much difference between them:
The actual data underlying the box plot isn't normally distributed and doesn't normalise well to transformations. They are roughly the same distribution (i.e. the YES and NO distributions for each algorithm), but the large data size differences make other tests a bit useless. I have applied the Two-sample Kolmogorov-Smirnov test, however this is probably wrong and it gives extremely significant results.
My questions are:
1) Do statistical tests on large datasets produce significant results given even slight differences between the two samples? The 'slightness' being magnified given huge data points.
2) Is visual inspection better with large datasets rather than applying non-parametric and parametric tests in which certain underlying assumptions may be violated.
3) For this data, what is the best course of action?Edit
My data has structure like :
My data is of the form:
Name Bind miRNA a 300 NO b 500 YES c 140 YES d 2345 NO