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This is a textbook example of how not to label and caption figures within a scientific publication. The caption for each figure should completely describe the

This is a textbook example of how not to label and caption figures within a scientific publication. The caption for each figure should completely describe the information being shown. In this case, they have a Time axis with numbers but no indication of what units of time are being represented, or what is meant by time '0'.

luqtas 1y ago
are you those smart geeks who check for images and read a paragraph of the conclusion? because i think the graph makes complete sense by checking the methodology... which i'll save your time: 18 years and ~ 16,000 participants
exmadscientist 1y ago
The graphs are, in fact, utter BS. They don't really match the method described in the "analytical approach" section particularly well. The real conclusions are in Table 2. "Pet ownership" is barely "statistically significant" (yeah, I know...) there. Tables 3, 4, and 5 should be similar but as a whole things are kind of all over the place. It's not a strong paper.
foobarian 1y ago
Let's not forget the Y axes not starting at 0!
- etrautmann 1y ago
  What’s wrong with that?
  - deleted 1y ago
    [deleted]
  - j_bum 1y ago
    Nothing inherently, but the problem is that it can be misleading. Let’s make an arbitrary example. Say a study is designed to measure the difference in lifespan between group A (water drinkers) and group B (coffee drinkers). Imagine there is a true difference in lifespan (say, 87.8yr vs 87.9yr) and the study is powered to observe a statistical difference. Now imagine plotting the average lifespans between the two groups. If the scale starts at 0 and ends at 100, the difference would be imperceptible. But if the scale is shown on a range of 87.6-88.0yr, with breaks of 0.05yr, the difference between the groups would appear quite large. This Y axis scale is shifting your frame of reference to potentially think the difference is bigger than it actually is. Maybe that 0.1yr is meaningful for some contexts or opinions, but maybe it’s not. This is why reading the units on an axis is very important: so we’re not mislead by the visual difference between two group values.

luqtas 1y ago

are you those smart geeks who check for images and read a paragraph of the conclusion? because i think the graph makes complete sense by checking the methodology... which i'll save your time: 18 years and ~ 16,000 participants

exmadscientist 1y ago

The graphs are, in fact, utter BS. They don't really match the method described in the "analytical approach" section particularly well. The real conclusions are in Table 2. "Pet ownership" is barely "statistically significant" (yeah, I know...) there. Tables 3, 4, and 5 should be similar but as a whole things are kind of all over the place. It's not a strong paper.

foobarian 1y ago

Let's not forget the Y axes not starting at 0!

etrautmann 1y ago
What’s wrong with that?
- deleted 1y ago
  [deleted]
- j_bum 1y ago
  Nothing inherently, but the problem is that it can be misleading. Let’s make an arbitrary example. Say a study is designed to measure the difference in lifespan between group A (water drinkers) and group B (coffee drinkers). Imagine there is a true difference in lifespan (say, 87.8yr vs 87.9yr) and the study is powered to observe a statistical difference. Now imagine plotting the average lifespans between the two groups. If the scale starts at 0 and ends at 100, the difference would be imperceptible. But if the scale is shown on a range of 87.6-88.0yr, with breaks of 0.05yr, the difference between the groups would appear quite large. This Y axis scale is shifting your frame of reference to potentially think the difference is bigger than it actually is. Maybe that 0.1yr is meaningful for some contexts or opinions, but maybe it’s not. This is why reading the units on an axis is very important: so we’re not mislead by the visual difference between two group values.