(I would also argue that dot charts make as much or more sense for such data.) (I would also be open to using some overall level, such as the national mean, as a reference.) Even some statistical people who have heard this little story have sometimes replied, "No bars should always start at zero." To me that is no better than irrelevant dogma in such a case.
I suggested that parity between males and females (1 or 100 females/100 males) was a much more natural reference level. That was correct, but the interesting story was that areas were different despite similarities, not that they were similar despite differences. All bars were close to the same length despite some considerable variation. The graphic was a bar chart with all bars starting at zero. A researcher was showing data on sex ratios for various states and union territories in India. Here is a true story from a presentation. In what sense whatsoever is it either helpful or even logical to insist on showing zero temperatures? Important, even medically or physiologically crucial, information will be obscured otherwise. Here is a simple example, but all three points arise: You measure body temperature of a patient in Celsius, or in Fahrenheit, or even in kelvin: take your pick. (Nothing rules out some tension between them on occasion.) Show zero on the $y$ axis if comparisons with zero are central to the problem, or even of some interest.
(If you are not the client or customer, talk to someone in the field to get an idea of what is interesting or important, preferably those commissioning the analysis.) ) judgement as well as your statistical judgement. Use your scientific (engineering, medical, social, business. I want to point out misleading presentation in papers I review, but I don't want to be a zero-y-axis purist.Īre there any guidelines that address when to start the y-axis at zero, and when this is unnecessary and/or inappropriate? (Especially in the context of academic work.)ĭon't use space in a graph in any way that doesn't help understanding. Instead, for context, show more data horizontally! The urge to contextualize the data is a good one, but context does not come from empty vertical space reaching down to zero, a number which does not even occur in a good many data sets. The scientists want to show their data, not zero.
MATHEMATICA 7 LOG SCALE Y AXIS HOW TO
(The book, How to Lie With Statistics, is wrong on this point.)įor examples, all over the place, of absent zero points in time-series, take a look at any major scientific research publication.
But don't spend a lot of empty vertical space trying to reach down to the zero point at the cost of hiding what is going on in the data line itself.
If the zero point reasonably occurs in plotting the data, fine. In general, in a time-series, use a baseline that shows the data not the zero point. For example, Edward Tufte points out that in a time series, the baseline is not necessarily zero: However, "always start the y-axis at zero" is not a hard-and-fast rule. When I review scientific publications, or students' lab reports, I am often frustrated by this "data visualization sin" (which I believe the authors commit unintentionally, but still results in a misleading presentation.) One common way to "lie with data" is to use a y-axis scale that makes it seem as if changes are more significant than they really are.
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