PDF for a continuous distribution If for a discrete distribution we can say that its PDF answers a question of something like "How possible is exactly this  value?", then what would a be similar "human" interpretation of a PDF for a continuous distribution?
And in R, how in some kind of "practical" terms can an illiterate like me interpret something like dnorm(0.2, 0, 1)? What exactly is '0.2'?
Besides, if dnorm reports a probability for a value in an interval, then is it in any way related to pnorm, which kinda also reports probability for a value in an interval (probability for a value in an upper or in a lower tails)? Can dnorm be expressed via two pnorms? Like, Probability(x1 < X < x2) = F(x2) - F(x1) or something like that?
 A: The Cumulative Distribution Function (CDF, sometimes just called the distribution function) of a continuous distribution (like the normal distribution) gives you the proportion of the distribution less than or equal to a given quantile (i.e., a given point in $X$).  

The Probability Density Function (PDF) is the derivative of the CDF.  That is, it gives the rate of change in the CDF (how fast it is increasing) associated with a given quantile.  

It does not give the probability of drawing that quantile from the distribution.  It can't, because there is no finite probability associated with a point from a continuous distribution.  Remember that in math / geometry, a point has no finite width, and probability is defined as the area under the curve.  Thus, we can take the height of the curve at a given quantile $f(x)$ and multiply it by the width, which is infinitely narrow (call it '$0$' if that's easier to think about), and the area will always be infinitely small.  This is not intuitive for people, because we think of a point being something like $1.2$, not $1.2\bar{0}$ (where $\bar 0$ means infinitely repeating $0$s); in other words, people think of $1.2$ as the interval $[1.15, 1.25)$, where any value within that interval is $\approx 1.2$.  There is a finite probability associated with that, because it does have a finite width (even if small).  However, because that has a measurable width, it isn't a point.  
