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Dogmatically insisting on origins-at-zero is silly: it depends too much on the data, the message, and the audience. For example, my lab studies the electrical

Dogmatically insisting on origins-at-zero is silly: it depends too much on the data, the message, and the audience. For example, my lab studies the electrical activity of brain cells. A neuron normally sits at -70 mV relative to the extracellular space. When it fires, it can briefly cross 0 mV (though it probably won’t reach +70 mV) and sometimes you might want to show that complete spike. Other times, subthreshold changes in the cells’ activity are more important to a hypothesis, and then you’ll zoom in on a smaller range (maybe -90 to -55 mV). Neither of these situations would benefit from a plot centered at zero; in fact, it would look decidedly odd. (Your other comment about log-log plots also seems odd to me, because the width of the line is almost never meaningful, unless there’s something like a confidence/credible interval band, which case that’s the whole point.

hmwhy 7y ago
I don't think the comment on log-log plots is limited to just log-log plots, and the point is that there are times when the width of the line of best fit, together with the size of data points, misleads the reader to think that it's more linear than it actually is. Problematic presentation of a similar type is particularly pronounced in, for example, finite-size scaled curves, where the perceived goodness of fit is heavily influenced by the line widths of the curves, the size of the data points, and the resolution of the plot. These misleading data representations are something that can be found even in recent publications in prestigious journals (I don't think it's always intentional and therefore won't include any references); also, sometimes it's difficult to notice there are issues unless you are very familiar with the methods, or have tried to reproduce the results (and have wasted months or even years of your life by then).
hinkley 7y ago
Clearly you have to 'zero out' a graph for whatever the default state is. If I claimed otherwise, that was in error. Yes, if the default value is -500, then setting your origin at 0 is going to cause confusion. I think the problem in part here is that in academia you can make your publishing quota for a period of time on a 2% improvement to some process. In which case you're amplifying the outcomes as a form of self-promotion. Rarely do software developers, city planners, or who knows what other professions are going to get any accolades or shifts in public opinion off 2% (unless it's taxes). In both cases you're trumping up the numbers. In one it's expected, and I think we can agree that things are perhaps not as they should be in the paper publishing circles without having to open that can of worms to define exactly why or in what ways.

hmwhy 7y ago

I don't think the comment on log-log plots is limited to just log-log plots, and the point is that there are times when the width of the line of best fit, together with the size of data points, misleads the reader to think that it's more linear than it actually is. Problematic presentation of a similar type is particularly pronounced in, for example, finite-size scaled curves, where the perceived goodness of fit is heavily influenced by the line widths of the curves, the size of the data points, and the resolution of the plot. These misleading data representations are something that can be found even in recent publications in prestigious journals (I don't think it's always intentional and therefore won't include any references); also, sometimes it's difficult to notice there are issues unless you are very familiar with the methods, or have tried to reproduce the results (and have wasted months or even years of your life by then).

hinkley 7y ago

Clearly you have to 'zero out' a graph for whatever the default state is. If I claimed otherwise, that was in error. Yes, if the default value is -500, then setting your origin at 0 is going to cause confusion. I think the problem in part here is that in academia you can make your publishing quota for a period of time on a 2% improvement to some process. In which case you're amplifying the outcomes as a form of self-promotion. Rarely do software developers, city planners, or who knows what other professions are going to get any accolades or shifts in public opinion off 2% (unless it's taxes). In both cases you're trumping up the numbers. In one it's expected, and I think we can agree that things are perhaps not as they should be in the paper publishing circles without having to open that can of worms to define exactly why or in what ways.