The normal distribution, or "bell curve," represents how scores or whatever we are measuring are distributed. In general terms, if we were to measure some attribute in the population, say a person's height, we might find that most of them are clustered near the average, while maybe a few are further away, either lower or higher. So, the higher the curve, the more scores there are in that range or region.

Knowledge of how scores are distributed then allows us to determine how likely or unlikely it is to obtain a particular score. The height of the curve, then, also tells us the relative probability of getting a particular score, or the probability or percentage of the population that falls within a certain range.

For example, if you measured the heights of adult males, you might find that most of them cluster around 68-73" but there may be a few below or above that range.

These concepts form the basis for statistical inference - when we draw a sample from the populaton, can we reach any conclusions about the shape of the distribution of the population? Also, if someone were to claim that the average adult male height is 75" or some other unlikely value, we could use sampling and the normal distribution to attempt to refute that claim.

Hope this helps a bit.....