Re. 20 samples being enough to achieve statistical significance. That is nearly true (a bit small for robustness in most circumstances) but the key is that it has to be REPRESENTATIVE data. If you have 60 days and 10,000,000 intraday data points, if it is a particularly quiet or particularly volatile period, it will not be representative of the true universe of possible distributions of prices that are likely to occur in real life. I teach statistics, and I often use this analogy: If I am doing a study on the height of average college students, and I decide that it would be convenient to take my sample at the dorm nearest my office. I walk into the cafeteria and survey 70 people, only to find that the average college male is about 6'10" and female is about 6'1". Of course, I have managed to hit the men's and women's basketball teams' training tables. I then do the same study with only half as many subjects, but selected at random over a 10 day period as they get out of their cars in the parking lot, and find that men average 5'10" and women 5'4" tall.
Price distributions in the markets are kurtotic, with "unlikely" events occuring more often than they "should" in lognormal distributions. Thus, the quality of your data sample, and how closely it represents the true range of what typically occurs in the real world is often far more important than simply the quantity. In fact, a large sample can achieve statistical significance while detecting effect sizes that are too small to be meaningful in the real world, and sometimes a smaller sample can actually serve your purposes better, by revealing only the more dramatic effects and producing less "noise".
Hope that isn't totally confusing.......
Jessie
Price distributions in the markets are kurtotic, with "unlikely" events occuring more often than they "should" in lognormal distributions. Thus, the quality of your data sample, and how closely it represents the true range of what typically occurs in the real world is often far more important than simply the quantity. In fact, a large sample can achieve statistical significance while detecting effect sizes that are too small to be meaningful in the real world, and sometimes a smaller sample can actually serve your purposes better, by revealing only the more dramatic effects and producing less "noise".
Hope that isn't totally confusing.......
Jessie