Before understanding the bell curve, you first need to understand what a distribution is. If you would take a bet on the direction of the market each day for a few months and write down your P/L at the end of each day, you could lump these P/L numbers into bins:
bin A: number of times P/L falls between -2% and -1%
bin B: number of times P/L falls between -1% and 0%
bin C: number of times P/L falls between 0% and 1%
etc.
the numbers you get for each bin are called frequencies and together they form a distribution (of your daily P/L).
If you would plot these numbers as bars (P/L on vertical axis, the bin on the horizontal axis), you would get a "histogram" with usually the tallest bars in the middle and the smallest at both ends.
If you would make the bin size very small and record your P/L for many days, a line drawn through the tops of the bars would start to look like a smooth curve.
You can get similar curves for other data, e.g.
- roll 10 dice each day and record their sum
- take the average IQ of ET members posting between 9-10am each day
etc.
If the curve meets certain statistical properties, e.g. when the underlying data come from a process similar to rolling the dice, the curve is called a bell curve, and the distribution is called "normal" or "Gaussian".
The nice thing is that as soon as you can call your distribution (the one you derived by recording your daily P/L) a bell curve, a lot of statistical properties can be easily computed from your data, e.g. you can predict the probability that one of the daily losses will exceed -5%.
But if it's not a real bell curve (as is often the case in the stock market), this prediction is much harder, and using the easier to obtain prediction from the bell curve can be very misleading if you aim to predict rare events ("black swans").