There are two laws on numbers which are often confused in understanding and used interchangeably.

**The Law of Large Numbers** states that if we repeat a random process, such as tossing a coin or rolling a die over a ** very** large number of times, the individual outcomes when averaged should be equal to the theoretical average. A more technical definition is that if you have repeated, independent trials with a probability of success

*P*for each trial, the percentage of successes that differ from

*P*converge to zero as the number of trials

*n*tends to infinity.

For example, if we were to throw a fair six-sided die with numbers 1,2,3,4,5,6, for say *1 million (!) times*, we would end up with an average of 3.5, which is the expected value.

**The Law of Averages **however is often confused with the Law of Large Numbers and many texts use the two terms interchangeably.

In fact, the Law of Averages as defined by the Cambridge Dictionary as: “the idea that over a period of time a particular thing will happen because it is just as *likely* to happen as the other possible events”. In other words, if we repeat a random experimental process, such as tossing a coin or rolling a die over a large *but* *defined* number of times, the individual outcomes when averaged should be ** very close (not equal)** to the theoretical average with a certain probability

*P*, however small.

These two laws are *basically* talking about the same thing. The Law of Averages is usually mentioned in reference to situations without enough outcomes to bring the Law of Large Numbers which is a statistical concept into effect.

A common example of how the Law of Averages can mislead involves the tossing of a fair coin (with equally likelihood to come up heads or tails on any given toss). If someone tosses a fair coin and gets several heads in a row, that person might think that the next toss is more likely to come up tails than heads in order to “even things out.” But, the true probabilities of the two outcomes are still equal for the next coin toss and any coin toss that might follow. So it is obvious that past results have no effect on the next toss at all. Each toss is indeed an independent event.

It is noted that the casinos around the world have made good fortunes by cleverly using the most misunderstood probabilistic theorem, the Law of Averages! Many people think they understand the Law but actually don’t.

Most of the well-known games casinos offer, including roulette, craps and slot machines, have outcomes whose probabilities can be calculated precisely from first principles. And, armed with these, casinos have created a business model based on payouts which seem reasonable or attractive if you are lucky enough, but actually aren’t. They are all less than they should be for a genuinely fair game – but, cunningly, most of them are not too unfair either. It is a combination that pulls off the remarkable trick of ensuring lots of punters keep coming back, whilst ‘the house’ still gets a rock solid profit margin.

Let’s take a look at the simple casino game of roulette, which contains a fixed set of probabilities with no real skill required to play the game. It comes with its famous wheel of 36 alternating red and black number ‘pockets’. As there are 18 of each color, it seems obvious that the probability of the ball landing in Red or Black is 50:50; certainly that is what the casino operators want you to think, as they pay out at evens odds to anyone who bets on Red or Black.

But if you were to take a closer look at the wheel, you would see that another numbered zero and colored green is tucked in among the red and black pockets. In the USA, there is usually an additional green pocket, numbered ‘00’. We may think it hardly important as we may notice that one can easily sit through dozens of spins without the ball landing on green. But a quick sum reveals something odd.

For a single zero roulette table, there are actually a total of 37 different possible outcomes and each outcome is independent of the previous one. For a double zero roulette table, the possible outcomes of course are 38. And, if you placed a $10 bet on Red and win, the casino will give back to you $20. However, the probability of a Red appearing is 18/37 or 18/38 (again depending on the type of table), which means to provide a fair payout for your winning bet, you should have been paid approximately $20.56 or $21.11, respectively.

Hence, those green pockets have tilted the benefits of the game towards the casino. Of course, the tilt is so slight – less than 3% – that it is easily swamped by the random fluctuations in the short term, such as the time spend at the table by most punters. Therefore, over the course of a few hours, some may win big and happily leave the table, others will curse their luck, but none would be able to detect the small bias in favor of the house. Indeed, the Law of Averages indicates that it would show up convincingly only after careful observation over at least 1000 spins. But, who would play for that long?

The casinos on the other hand, via many dozens of wheels, running 24 hours a day, 365 days a year would reap handsome benefits from this ‘house edge’ or solid margin of 2/38^{th} or 5.3% from all the Red/Black bets (or 1/37^{th} or 2.7% from a European wheel).

Furthermore, The Law of Averages also applies a fallacy to many gamblers in casinos. Many gamblers tend to mistakenly believe a losing streak will ‘even out’ in the end. It would not be so. Consider a roulette wheel that has landed on Red in five consecutive spins. The punter might apply the Law of Averages to conclude that on its next spin it must (or at least is much more likely to) land on Black and puts up a heavy bet on it. Of course, the wheel is mechanically operated and has no memory and so its probabilities do not change according to past results. Indeed, the probability of getting a Red or Black on any spin is about 1/2, no matter how many times it has spun. Even if the ball of the wheel has landed on Red in ten or twenty consecutive spins, the probability that the next spin will be Black is still no more than 48.6% (assuming a *fair* European wheel with only one green zero); and 47.4% for a fair American wheel with one green “0” and one green “00”. Of course, it would be exactly 50% if there were no green zero and the wheel were fair.

The simple reason is that the Reds and Blacks would not even out by having just a small amount of spins, whilst it is true that in the *long run*, the proportion of Reds and Blacks will finally even out but in the short run, anything is possible. Even if you stay at the wheel for a couple of hours and bet 200 times, that is still a relatively small number (as compared to a million, for example) and the Law of Large Numbers says that if you are able to stay at the wheel for an infinite amount of spins (say a million, if possible), you will even out the spins to 50% black and 50% red if there were no green zero with fair wheel. But this is impossible in real life!

Similar situation is observed in the ever popular 4-D or *Totto* lottery draws. There is no statistical basis for the belief that lottery numbers which have not appeared in a while are due to come up soon. In fact, any one number is just as likely as the next to come up during the public draw. Only the smiling lady luck can help you spot on betting a selected number or numbers!

*We shall discuss if there is any real chance (statistically speaking) of hitting a big jackpot in casino slot machines on the next blog. *

Comments on:"Gambler’s Fallacy on The Law of Averages" (1)slotnextspin01said:Wow! Excellent Post. I really found this so much informatics. It is what I was searching for I would like to suggest you that please keep sharing Such type of information. Thank you so much.