The basic premise is that the plot compares the data with what would be expected of data that is perfectly normally distributed. Then two quantities are compared: The data and idealized normally distributed data. If the two generally agree that means the data agrees with what would be expected from a normal distribution. The normal probability plot is then linear. Otherwise, the plot will not be linear. Of course, no plot will be exactly linear, because data is subject to randomness in it's collection. We llok for a general pattern of linearity.