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The dog weight one is a good case of how you can futz with numbers to imply a greater relationship than is present. I think the second chart is much better in

The dog weight one is a good case of how you can futz with numbers to imply a greater relationship than is present. I think the second chart is much better in that respect. If you're doubling the relative range of one of a double scale to make stuff look like it fits, that's not exactly being forthright. > but the original charts make it easier to see the relationship between the two series. Well, as the article says, if it looks like they're a perfect fit when they definitely aren't, that's probably beyond making something clearer and going into convincing people of something that the chart doesn't really imply. > the last chart is fine. It's maybe not very sexy, and it takes a moment to absorb, but the data is clearly displayed. Totally agree on this. It takes a bit to digest, but that's because there's a lot of information imparted.

function_seven 8y ago
There's no reason the scale needs to be the same when comparing measurements in totally different dimensions. The weight is roughly a volume measurement while the neck size is a linear one. Also, if the necks were measured in inches, you'd end up at roughly the original chart, even being mindful of the scaling on each axis. I think the left chart is actually the better one. It does a good job showing the correlation between neck size and weight.
- Quenty 8y ago
  It’s a matter of percent change. It shows the same relative percent change on the scale, which feels more accurate. I prefer the revised graph.
  - gowld 8y ago
    > > The weight is roughly a volume measurement while the neck size is a linear one. so "percent change" is a different dimension in the two data sets, respectively.
ovi256 8y ago
>The dog weight one is a good case of how you can futz with numbers to imply a greater relationship What they did was very similar to standardizing data series (normalizing to mean=0 and standard deviation=1). This is a standard tool in scientific data analysis (most published papers use it) and it's taught in statistic and data science classes. It's also a preliminary step in computing most correlation measures. It does not exagerate relationships, it helps visualizing them. The second chart removed the standardization and obfuscated the relationship.
- SiempreViernes 8y ago
  As you can clearly see from the axis labels, they are not plotting standardised or indexed data, what they did was hand picked a rescaling that makes the plots line up very nicely. Further, it is simply not true that weight is a perfect function of neck size, so you shouldn't make a plot that implies that.
  - gowld 8y ago
    The reason it's hard to pick which chart is better is that both charts are far too detailed for the tiny amount of information they convey, which is "both values shrank over time"
    - SiempreViernes 8y ago
      Can you further explain the frankly astonishing statement that you find it hard to pick between a plot showing a false equivalence and one that doesn't?
  - ovi256 8y ago
    >what they did was hand picked a rescaling that makes the plots line up very nicely I bet dollars to donuts that's what a stardization would show too, unless I'm missing something. It would show an even more perfect lining up because it would line up the line ends. Wish I could test it. Wish pandas had a read_image function to get data from line plots like it has read_html.

function_seven 8y ago

There's no reason the scale needs to be the same when comparing measurements in totally different dimensions. The weight is roughly a volume measurement while the neck size is a linear one. Also, if the necks were measured in inches, you'd end up at roughly the original chart, even being mindful of the scaling on each axis. I think the left chart is actually the better one. It does a good job showing the correlation between neck size and weight.

Quenty 8y ago
It’s a matter of percent change. It shows the same relative percent change on the scale, which feels more accurate. I prefer the revised graph.
- gowld 8y ago
  > > The weight is roughly a volume measurement while the neck size is a linear one. so "percent change" is a different dimension in the two data sets, respectively.

ovi256 8y ago

>The dog weight one is a good case of how you can futz with numbers to imply a greater relationship What they did was very similar to standardizing data series (normalizing to mean=0 and standard deviation=1). This is a standard tool in scientific data analysis (most published papers use it) and it's taught in statistic and data science classes. It's also a preliminary step in computing most correlation measures. It does not exagerate relationships, it helps visualizing them. The second chart removed the standardization and obfuscated the relationship.

SiempreViernes 8y ago
As you can clearly see from the axis labels, they are not plotting standardised or indexed data, what they did was hand picked a rescaling that makes the plots line up very nicely. Further, it is simply not true that weight is a perfect function of neck size, so you shouldn't make a plot that implies that.
- gowld 8y ago
  The reason it's hard to pick which chart is better is that both charts are far too detailed for the tiny amount of information they convey, which is "both values shrank over time"
  - SiempreViernes 8y ago
    Can you further explain the frankly astonishing statement that you find it hard to pick between a plot showing a false equivalence and one that doesn't?
- ovi256 8y ago
  >what they did was hand picked a rescaling that makes the plots line up very nicely I bet dollars to donuts that's what a stardization would show too, unless I'm missing something. It would show an even more perfect lining up because it would line up the line ends. Wish I could test it. Wish pandas had a read_image function to get data from line plots like it has read_html.