# Probability of a value x in a Normal distribution is not zero but some value

I'm totally confused about the concept of Probability Distribution Function for continuous sample space.I read that probability of an event in continuous space is zero and it seems logical since we have an infinite number of points also as quoted by Wikipedia the absolute likelihood of an event exactly occurring is zero. But when we substitute a value x in the Normal distribution function we get a finite value . Can someone explain to me whether we can use PDF for a exact value or should we consider only a range to compute the probability of a range? Also when we substitute mean in the Gaussian the value seems to be very high indicating the mode and mean are the same.

Edit: Thank you for the answers but when we write the probability of data set given the mean and variance in the maximum likelihood function, we actually use the Normal distribution PDF directly for a input vector X which is N(x|mean,variance) , here we are using the PDF as if to represent the probabilities of the input vector.

• Having an infinite number of points in the space does not by itself produce the result that each point has zero probability. Consider the geometric distribution, which has positive probability on each of a countably infinite number of points. It's when you have an uncountably infinite number of points that you can't assign positive probability to each point. (You can still assign positive probability to a countable subset.) – mef Sep 25 '17 at 15:04
• The PDF is a probability density. It is not a probability! Please see stats.stackexchange.com/questions/4220. – whuber Sep 25 '17 at 15:38

This value is not a probability in itself, but rather the rate of change of some probabilities. If you want to think of it in terms of probabilities, then the PDF at $x$ is
$$\lim_{\Delta x \rightarrow 0} \frac{P(X < x + \Delta x) - P(X < x)}{\Delta x}.$$
This does not contradict $P(X = x) = 0$ for all values of $x$.