Value of logarithmic form I'm having trouble grasping the viability/value of log'ing a dataset. This post mentioned that it's used to normalize (read: shrink extremes of) a dataset and make it easier to fit a curve. But, doesn't that just distort the data? Is this a trade-off that just warps the data when it comes to the outliers to make it a more generally-useful curve?
Please forgive any misused vocabulary. I'm an engineer, not a statistician.
 A: The question you link to doesn't seem to say anything about fitting a curve to data. The answer says "fit them on the same curve" which is actually talking about displaying the data (see the reference to tick marks), and the graph they're discussing (at the linked article). 
[I wouldn't have used the word 'curve' there, myself.]
When it's made clear that the data are log scale (and that should be made clear on the plot, not just in the text), what is being distorted exactly? 
From what I've seen log-log plots are used widely in engineering applications, and often for much the same reasons it might be done here.
What they are useful for is making power-relationships linear (the power becomes a slope) and facilitating comparisons in percentage terms (on both variables).
Besides making relationships into a simpler form, which may be more easily discussed, when data span several orders of magnitude, it can be the case that a few large values make it very hard to perceive the broader relationship at all; then a log-transformation may reveal what would otherwise be hidden.
Note that the text discussed the effect of a certain percentage increase in terms of a certain percentage increase in the other variable ("1 percent increase in population generates a slightly smaller (between 0.8 and 0.9 percent) increase in emissions"); this information would correspond to the slope of a line on the log-log graph. 
Rather than distorting, it makes it easier to reveal a particular kind of information. One reason why is discussed in the answer to the question you linked to.
