I guess your best bet would be to apply Int() with a scaling factor (i.e., Int("raster" * 1000) ). You would then convert the resulting raster to a point feature class and divide the attribute field by the scaling factor to coerce the data back to floating point. You could then join each resulting converted point feature class. This will give you want your are after but is highly inefficient. I would also make the argument that, at this point you have a population and not a sample, which will cause notable issues with your statistical analysis.
The more prudent, and considerably more efficient, approach would be to figure out the sample size required for an adequate sample distribution of the data. You can then create a random point sample, representing the derived optimal n, and use this to sample all of the KDE rasters. The results would be a point feature class with columns for each raster. You could then simply export the attributes as a table.
You would use the following tools:
1) Data Management Tools > Feature Class > Create random points
2) Spatial Analyst Tools > Extraction > Extract Multi Values to Points