I often see graphs that contain a time series of some sort, but the numbers are just plain numbers, not normalized by population. Here’s an example of a graph from the Bush-era, flaunting the supposedly beneficial effects of Bush’s labor policy on job growth (green line, “jobs on the rise”, number of jobs in thousands):
Just presenting the numbers of job without relating them to the population, is meaningless. Maybe the population grew faster than the number of jobs, in which case the growth exhibited here is in fact a decrease. Or the population shrunk, in which case the growth in the number of jobs was even bigger.
Here’s the correct graph, showing that employment did increase under Bush, but decreased during the last years of his presidency:
“Population” can mean actual population (i.e. people or residents), but can also mean any other relevant basis of comparison. For example:
The following statistics suggest that 16-year-olds are safer drivers than people in their twenties, and that octogenarians are very safe:
As the following graph shows, the reason 16-year-old and octogenarians appear to be safe drivers is that they don’t drive nearly as much as people in other age groups:
Another example is the national debt statistic. Often the graph shows just the national debt in dollar, without relating it to GDP. Whereas the absolute amounts do have some relevancy, it’s better to express the debt as a percentage of GDP because a bigger economy can carry a bigger debt (a poor household may go bankrupt with a debt of $10,000, whereas a rich household can live with a debt of perhaps $100,000).
Take this graph for instance:
Now compare it to this one:
Or this, slightly more recent one, including the latest recession:
And a final example: looking at the relative safety of air travel and road travel and the probability of dying in either a road accident or a plane accident, you can also find divergent data depending on how you divide: number of casualties per trip, per miles traveled, per hours traveled etc.