Quite frankly, I think ye all doth protest to0 much. The choice of how to
present or display the data, depends on what you are trying to elucidate. If
one is looking for rare events, I've found the dot plot with the proper
logical gates to be invaluable. For separating subsets with very similar
charactaristics, contour plots, with as little smoothing as necessary have
been my choice. With enough events, the 3-D plots can be very revealing
particularly in combination with logical gating and 3 color staining. The
combination of contour and dot outliers (or is it outliars) sounds like fun,
but I don't have that one available yet. Histograms also have their place for
certain kinds of data, energy transfer, FISH, etc.
Assuming that the original data is collected and compensated properly, all
these data displays are merely tools allowing us to make interpretations of a
mass of information. Just as some data is better understood by expressing it
on a log scale rather than a linear one, different types of displays allow us
to demonstrate different points. We all have our own little pet preferences
for how we like to display the data. While I like a good colorful multicolor
presentation of data as well as the next flower, my administrative types,
certainly do not like the page charges for color printing in journals and
reprints.
I think that among the main questions are-- Does the data display support the
interpretation that the author or speaker is expressing?. Yes or no? If it
does, without stretching the bounds of believablity, then that's good enough.
There are times when one really does not want to get into all the little
subgroups that a contour display might disclose.
Not all of us chose to interpret data in the same manner, nor do we all have
the same need and level of understanding. Some people are splitters -- always
looking for the differences, i.e. more and more subgroups, etc. Other people
are lumpers -- always looking for similarities and universality in data and
across disciplines. There is a room and a need for both. If one has a real
problem with a colleague's data, one can always repeat his experiments and
subject it to his own analysis and interpretation, or be collegial and suggest
that the data be analysed using a different display of the same data.