probability density functions let' talk about probability density functionsand we've used one of these already in the book we just didn' call it that let' formalize some of the stuff that we've talked about for examplewe've seen the normal distribution few timesand that is an example of probability density function the following figure is an example of normal distribution curve it' conceptually easy to try to think of this graph as the probability of given value occurringbut that' little bit misleading when you're talking about continuous data because there' an infinite number of actual possible data points in continuous data distribution there could be or or so the actual probability of very specific value happening is veryvery smallinfinitely small the probability density function really tells the probability of given range of values occurring so that' the way you've got to think about it sofor examplein the normal distribution shown in the above graphbetween the mean ( and one standard deviation from the mean ( sthere' chance of value falling in that range you can tighten this up or spread it out as much as you wantfigure out the actual valuesbut that' the way to think about probability density function for given range of values it gives you way of finding out the probability of that range occurring you can see in the graphas you get close to the mean ( )within one standard deviation (- and )you're pretty likely to land there meanif you add up and which equals to %you get the probability of landing within one standard deviation of the mean