Parametric statistics for flow

Hi folks

I am analysing some histogram data for hit detection assays on beads 
and am looking for comments about the analysis. To perform hypothesis 
or inference tests on the histogram data, I must somehow suggest (in a 
way that is at LEAST semi-quantitative) that my data correlates with a 
parametric distribution - eg normal, exponential, weibull, etc etc. I 
am well aware that a simple flow histogram is multivariate - ie the 
fluorescence response is probably based on several things including 
size, biomolecule desnity on the beads, etc etc. However, at large 
sample sizes, such data MUST approach some limiting distribution.

However, I come up against the problem that my data looks great when 
plotted in a Quantile/quantile plot (against theoretical NORMAL 
quantiles) but will perform poorly in statistical tests for "normality" 
such as kolmogorov-smirnov and shapiro-wilkes tests. I always keep the 
sample size >500. Are such tests necessary for convincing people about 
the data? How about just reporting the p-values for the KS tests - 
certainly not >90%, but always between 10 and 100%.

It seems that most people simply "assume" that the data "should" be 
somewhat normal and then go ahead and do t-tests, which will, I 
suppose, soak up some error, but still not quite sure how to convince 
myself that such methods are viable. If any statistically-minded people 
have some opinions, please let me know. I am keen to do this analysis 
properly and publish the findings - however good or bad they are ;)  

Regards

Simon Corrie