4 ms·

>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

by 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.
  - TeMPOraL 8y ago
    > Wish I could test it. Wish pandas had a read_image function to get data from line plots like it has read_html. Below each chart discussion in the article there's a link to the data, which in this case points to: https://infographics.economist.com/databank/Economist_dogs.csv https://infographics.economist.com/databank/Economist_dogs.c....

by ovi256 8y ago

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.
- TeMPOraL 8y ago
  > Wish I could test it. Wish pandas had a read_image function to get data from line plots like it has read_html. Below each chart discussion in the article there's a link to the data, which in this case points to: https://infographics.economist.com/databank/Economist_dogs.csv https://infographics.economist.com/databank/Economist_dogs.c....