What I find even more fun to look at are the 3-D plots, of course these also can be smoothed until they are as meaningless as any of the others. 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.
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