In addition to the lab, the issue of distribution also affects my day-to-day money management. I'm a grad student who likes to invest my modest stipend in stocks. As someone who is fairly new to trading securities, one comfortable way into the world of Wall Street was via statistics. The same challenges in statistics which face scientists also face economists and stock traders. When there is real money on the line, it helps to have some of these statistical tools on my side, to help temper any bias. Given that I am trading in real companies with significant reputations, it's hard not to get emotionally involved in any of my holdings. However, if I can set some rules and use some of the tools in this section of the text, I can increase my overall profit through bullish and bearish. For example, check out this Forbes article which underscores how a normal distribution of the DJIA 30 indicates a healthy market (also see figure).
Displaying an individual stock's performance in what we hope is a normal distribution or finding standard deviation can indicate the volatility of a stock, correlated to risk. This value can also be similar to beta, the value many investors use to indicate volatility/risk, although it should be mentioned that this is not how beta is calculated. Lastly, many trading algorithms on Wall Street and financial theories anticipate a gaussian distribution of stock prices in order to maximize alpha or gains adjusted to the overall performance of the market. Thus, skew and kurtosis are important for investors to keep track of! The role of normal distribution, skew, and kurtosis in investing is simply summed up in this post, for those interested.