Comment by hinkley | Hacker News Reader

Comment by hinkley | Hacker News Reader

Everyone needs to be better at data visualization. If for no other reason than to be an informed consumer. If Mark Twain had lived through informatics I'm sure it would be four ways to lie: "Lies, Damned Lies, Statistics, and Charts"

There are a lot of bullshit techniques used in data viz that are tantamount to lying and people are often sincerely shocked when you call them on it.

"I didn't do that on purpose," as if they didn't learn when they're 4 that it doesn't matter if you meant it if you did it. You still have to apologize and try to make amends.

Things people do from ignorance or malice:

Remove the origin on the graph and the relative height of the lines is skewed. 3d pie charts are 'larger' on the bottom half. Circle charts conflate diameter with area (humans are bad at judging area). On a log scale plot, a fat enough line can make anything look like a trend, because the end of the line is literally orders of magnitude wider than at the origin.

Friends don't let friends use any of these techniques, and the jaded instantly distrust anyone who is using them.

The used bookstore on the closest university campus has an entire shelf full of old editions of Edward Tufte books. I don't know if you'll be so lucky, but it's well worth a shot.

I agree that these techniques are often used in popular media in deceptive ways, but a couple of those plotting techniques have valid use cases.

Specifically, if you're really concerned about a delta, removing the origin is good for visualization; it lets you just see the difference between two trends (in particle physics, you can show an energy excess this way). Likewise, for exponential phenomena, log plots are the correct choice, since uncertainty will be magnified for larger values. In both cases, of course, you need to include error bars, but that is always true. But I don't think you can "instantly distrust" someone who is using these techniques when they are valid.

Most graphs with deltas are comparing multiple deltas, not a single delta, which results in misrepresentation.

By screwing with the origin you can make the alternative that saw a 6% reduction look much more compelling than the one that achieved a 4% reduction, when in fact it's probably only slightly more compelling.

Yes, I agree that these are often abused tactics. Just saying they have their uses. The fundamental problem is graph literacy.

You should be specific about who you’re calling out. I’m sure this is good advice for journalists making infographics for the public, but it isn’t for scientists talking to other scientists.

I design my plots for readability and in the process regularly break every rule you listed here, because I trust my audience (other scientists) to understand how to read axes. Yes, it might be confusing to somebody who doesn’t read graphs with axes that often, but if I optimized my papers for their convenience, they would all have to be 100 pages long.

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.

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).

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.

Sorry, can you explain / show an example of the log plot issue?

agreed. in working on uPlot, i've explicitly tried to stay away from misleading, hard to interpret, but pretty shit like line smoothing, stacked areas, etc.

https://github.com/leeoniya/uPlot#non-features

Reply on news.ycombinator.com