Parametric statistics for flow

David Coder d_coder at
Tue Nov 29 23:41:13 EST 2005

You don't mention if your data are collected/displayed as logarithmically
scaled histograms. A log transform of the data can 'look' Gaussian, but it's
not always the case. (See Coder DM, Redelman D, Vogt RF. Cytometry. 1994 Jun
15;18(2):75-8. Computing the central location of immunofluorescence
distributions: logarithmic data transformations are not always appropriate.)
Non-parametric tests (e.g., the KS test) very often give significant
differences given the large sample sizes. A trip to a local statistician
should help sort things out. (The University of Queensland has a Centre for
Statistics that deals with biostatistics.)

David M Coder, Ph.D.
Consultant in Cytometry
Irvine, CA 
Cell/Msg: 206 499 3446
Email: d_coder at

-----Original Message-----
From: Mr Simon Corrie [mailto:s369338 at] 
Sent: Monday, November 28, 2005 12:15 PM
To: cyto-inbox
Subject: 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 ;)  


Simon Corrie

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