Referring to my previous post about gambler’s fallacy, I was totally wrong after I pondering more about this.

In an example of tossing a coin, we know that to get a “tail” is 0.5 probability and “head” is 0.5 probability. That means, each result should fairly appear once. And in the experiment, if we tossed the coin 1000 times, then we will get the result of “tail” appeared around 500 times and “head” another 500 times.

The probability subject is a very difficult subject to me. This is because it involves estimation of all the possible events. Therefore, it involves the combination and permutation. And there is no exact formula for different situations. It also involves statistics.

Gambler’s fallacy, is a very good notion. To simplify it, gambler’s fallacy is a belief that the next outcome will be different if the observed outcome is repeated consecutively, where these events are actually independent. The best example is tossing the coin, which has the probability of 0.5 for head and 0.5 for tail. Because tossing the coin first time will not affect the second time, the probability to get the head or tail is always same.

Today, I read the newspaper, and found that there were a lot of people discussing this questions. You can read it from Yahoo! Answer. And there are a lot of people answering this expression equal to 9:

6/2(1+2) = 9

I really don’t understand the possibility of answering it as 9. Some of them said based on PEMDAS, it must be 9; also other said based on distributive property, it is 1.