I understand (not least from the below link, that i need to transform my data to arcsin and then analysis it as you would any other data set. This is correct isn't it?
[Strictly speaking it's not an arcsin transformation, but an arcsin-square-root transformation -- but your terminology seems to be used by quite a few people. This is an approximate variance-stabilizing transform for a binomial proportion.]
Generally speaking, there are better ways to analyze proportions than via the arcsin-square-root transform.
One problem is that for it to make variances approximately equal, you need the same denominator on the proportions. If that's not true, then you don't get homoskedasticity from the transformation (at least not on its own).
A second issue is that it's possible for model predictions to go "outside the bounds".
It's more common these days to use binomial GLMs to analyze such proportion data; it deals with different $n$ quite naturally, and respects the limits on proportions, even when predicting outside the range of the data. It also has better power, and tends to be somewhat more interpretable.
However, there may sometimes be some argument for using it in displaying data when trying to make visual comparisons of proportions; in that case the variance stabilization may be more helpful in some specific situations, but there are other things that might be done.
If you do a plot of arcsin-square-root transformed data, you can still mark the axis with the actual percentage values that the observations correspond to. It's just that each change of say 10% won't be equi-spaced on the axis.
For example, does 8%, once transformed, become arcsine 16.48 or can I still describe it as 16.48%
It is NOT 16.48%. Neither is is "arcsine 16.48". Where possible, call it 8%. You could call it 'a transformed value of 16.48', perhaps, except I don't see how you get that 8% transforms to 16.48:
The units would be radians. Hmm. Maybe you mean to do it in degrees:
Well, that's closer. So it's 16.43 degrees. Not percent.