If you wish to see specifically why, watch the 8 minute clip from minutes 12:15-20:50.

This shows that if we can put enough spins through our system, the results will conform to predictable results (win).

The reason why our overly-antagonistic AP "friends" don't wish to advocate ideas such as this, is because they live in a world where their day to day survival is based on knowing with 100% certainty that roulette cannot be beaten; this allows them to take greater comfort in their difficult and dangerous AP methods.

Why do they attack us? Simply to exalt their own ego and/or promote their AP systems.

This video (the marble example in the first 12 minutes) also shows why it is so difficult to beat roulette. If only roulette would conform to basic probability in our short sessions we would easily surpass the HE every time. The problem, as shown by the marble example, is that the random flow is only certain to conform to probability in the long term (millions of spins) but in the short term (our sessions) a very great potential for variance exists which is why we get hit so ubelievably hard and it seems that all probability and statistics no longer even apply.

This variance CAN be tamed as proven by the indicated clip in this video.

This video also proves (through all the examples) that there IS a governing force in roulette; the Law of Large Numbers must still apply in our sessions otherwise the law would not exist; this means there IS a limit to the variance that we are allowed to experience.

Our AP friends will of course cry, "Impossible!" but that is because their very survival depends on being negative about anything except AP. Those of us with unjaded and broad enough minds to grasp this potential will continue to work undaunted by our naysayers.

`0:13yeah`

0:17and order random event is number effect indeed can that large be overall of

0:31events are very event no out predictable of which such the occurred with yet very

0:41unpredictably dr. you just told you what this film is about he wrote a sentence

0:50to explain what we want to show you and then he cut the sentence up into

0:53individual words put the slips of paper into a half and threw them out in a

0:58random order and read them to you in this random order

1:02here's the sentence before it was scrambled random events are events which

1:10occur with no order that is unpredictably and yet the overall effect

1:15of a very large number of such events can be very predictable indeed who's

1:22going to start well let's use a random event to the side as the tail tail head

1:31you lose you start here are random events occurring naturally see the

1:42needle hear the click this is a Geiger counter and this contains a radioactive

1:49material polonium every time the Geiger counter clicks it means that i'm adam of

1:56polonium change into an atom of LED accept that there are some extra flex

2:01because of cosmic rays

2:04I said that the flex our random events

2:07what does that mean it means that there's no order to them

2:11I can't predict when the next one will occur even if i measure the time between

2:17place very accurately for a large number of clicks I will still not be able to

2:22predict when the next one will occur

2:25that's what i mean by a random event

2:28here's a picture from your textbook a grasp of the activity of a sample of

2:37polonium plotted against time in days you can see that the activity that is

2:43the number of radioactive disintegrations in the sample decreases

2:47as time goes on the activity at the start is 100-percent it's only one half

2:58as much at the end of 138 days at the end of another 138 days it's half of

3:09what it was at the end of the first 138 days it's down to one quarter of its

3:15original activity and this goes on every 138 days sees the activity cut in half

3:23this length of time is called the half-life of polonium all radioactive

3:30substances behave in the same way

3:33some have very short half-lives here's helium six a radioactive isotope of

3:42helium has a half-life of eight tips of the second some have very long

3:49half-lives here's uranium 238

3:56half-life is four and one-half been in years

4:03as you can see if a graph of activity is spotted in terms of half-life times it

4:09is identical for all radioactive substances this is a law a law of

4:17radioactive disintegration a description which fits the behavior of a great

4:23variety of substances and allows us to predict their activity at any time in

4:29the future

4:31how can that's predictable behavior emerged from the random unpredictable

4:36behavior the doctor IV showed you

4:38well we'll come back to radioactivity later but first we're going to

4:44investigate some other examples of random behavior would you care to

4:58predict where the next marble go the same place

5:06no I'll try a few more unpredictable than that there are 100 marbles in here

5:22I'll got them all

6:02yeah

6:15most in here maybe that's the slot that the marbles are most likely to go into

6:22I'll mark the way that the marbles are distributed here

6:37I'll put the marbles back in the tube

6:51ok

6:53now I'll do it again

7:32you can see that the distribution is not going to be the same

7:43this time there are most marbles in this slot

7:46perhaps now i might guess that the marbles are more likely to go into the

7:50slot here but that's all it would be a guess I still haven't made enough

7:55observations of the behavior of this apparatus to make reasonable predictions

7:59about what will happen so I'm going to take some statistics on the apparatus

8:03that is i'm going to observe what the behavior is in a systematic way I'm

8:10going to drop a hundred Marbles many times and each time draw a graph like

8:15this of the distribution and dr. Hume will help

8:19well let's get to work

8:46here are the 10 distributions we found in ten tries you can see that they're

8:58all different

8:59I'll put three of them together so you can compare them

9:13yeah

9:15yeah

9:18now you can see the sort of fluctuations that there are between them

9:23this graph shows the next thing we did we added together these ten distribution

9:33and we / 10 to make this graph the same size as these graphs this is now the

9:41average distribution for the ten tribes one way of looking at this is that this

9:46is the distribution we would have found if we dropped a thousand Marbles all at

9:50once the apparatus except that we couldn't because the apparatus won't

9:54hold a thousand Marbles all at once then we did this again drop a hundred Marbles

9:5910 * got another marble distribution and once more there it

10:12these three are hundred marble distribution and these three are

10:19thousand marble distribution you can see that the fluctuations here are much

10:25smaller than the fluctuations here if we dropped a million marbles at a time in

10:32the apparatus then we probably wouldn't be able to see any fluctuations call

10:36each of these graphs is a frequency distribution and the point of taking a

10:42lot of statistics for the apparatus is to get the best approximation that we

10:47can to the true frequency distribution for the apparatus the average of these

10:54three will be reasonably close to the truth frequency distribution here it is

11:01average

11:05now that I have this i can make predictions statistical predictions

11:13about the behavior of this apparatus you can see that the frequency here is about

11:23twice that of the frequency here

11:27this means that the probability of a marble going in this lot is about twice

11:34that of a marble going in this lot

11:46it didn't go in either one you must realize that a single marble still

11:51behave unpredictably this is the probability of a marble going in here is

11:57twice that of a marble going in here just means that if I drop a very large

12:01number of marbles twice as many of them will go in here as go in here and as

12:08you've seen very large number means just that you've seen how to find the

12:15frequency distribution for a simple pinball machine

12:18what about this machine it has 16 squares of cardboard mounted so that

12:26they can spin around one face is white and the other face flat and the light

12:33scattered from these squares can be read on a light meter over there and the

12:38results projected on the screen right now half of the squares are white and

12:44half of them are black and the reading is eight now dr. IV is going to turn the

12:50squared around so that they are all right side of now yes no

12:55we've made the scale on the meter so that reads directly the number of white

13:04square facing out the reading now is 16 now we better check up on the all-black

13:13reading

13:28the reading is zero no white square facing out

13:31dr. IV is going to start the square spinning now with a fan

13:38he has to help some of them along by hand

13:43I want them to end up facing out so he's sliding screen across the back

13:53the reading is eight now we're going to do this again several times and just

13:59show you the results

14:2311

14:29yeah

14:35796 and damn you might expect that the most probable result with the eighth

15:14corresponding to have black and half white but we get considerable

15:18fluctuation from them six but we'll have to do that the large number of times in

15:26order to get proper frequency distribution I don't think I have the

15:31strength

15:33well don't worry about that because for this machine I can calculate the

15:37frequency distribution of so you when I spin one of these cards around it comes

15:43out either black or white and I can't predict which I can't see any reason why

15:49I should come up white instead of black so first of all I assumed that these two

15:55alternatives are equally probable now these squares been quite independently

16:01of each other so the final result is the overall effect of 16 independent random

16:08events how do I calculate the probabilities of the 17 different

16:14possible results there's only one way to get all black or all white

16:21so these two meter readings are equally probable and they're certainly not very

16:28probable but look at the arrangement of square

16:31earth right now the probability of this particular arrangement is exactly the

16:37same as that for all black or all white but the meter reads the overall effect

16:43and can't tell the difference between this arrangement and any other with 10

16:48white and six black so the probability of a particular meter reading depends on

16:54the number of different ways the reading can be produced there are 16 different

17:01ways of getting one white or one black so these two meter readings are equally

17:07probable and 16 times more probable than this you may be able to go on now and

17:17calculate how many different ways there are getting two white or two black there

17:23are 120 different ways now i'm already off scale here when I plotted this

17:30frequency distribution before and here it is too much reduced scale you can see

17:38that the ones that I was calculating before hardly show here at all and that

17:45the eight is the most probable perhaps you can try working this out for

17:50yourself but you must remember that when you do make a calculation of this sort

17:56that you should do experiments to check it now over here i have a similar

18:02machine with spinning squares here there are 256 cards in the same area

18:12the overall effect of this one is made up of a much larger number of

18:18independent random events

18:21what about a frequency distribution for

18:26416 cards are reading of eight is more probable than the other for 256 cards

18:36are reading out eight is very much more probable than any other reading in fact

18:42it is so much so that i can almost say with certainty what the result will be

18:46when I spend the cards i predict a reading of eight

18:50let's draw

19:05ok

19:18ok

19:22text my prediction

19:28let's do it again

19:42eight

19:56yeah

20:00eight

20:08eight again

20:24the fluctuations are very much smaller here this reading is predictable

20:35that's why we said in the beginning that the overall effect of a large number of

20:40random events is very predictable

20:45now at last we are in a better position to talk about this law of radioactive

20:51decay that we started with first of all what does it mean to talk about activity

20:57it should mean that the sample of radioactive materials has a definite

21:03predictable number of disintegration in a certain length of time often dr. Hume

21:10speak of a predictable number of disintegration in a certain length of

21:14time when the disintegration are random i'll show you with this

21:20it's a Geiger counter which displays the number of columns here i'll start 12345

21:32as you can see the time between count is not predictable when there are 10 counts

21:38then a one comes up in the tens column and the unit column starts over now i'm

21:43going to move the radioactive polonium here closer to the detector this will

21:50increase the number of cum the unit com is still random but much faster than

21:57before the tens column is pretty random but what's the hundreds column 600 700

22:03800 900 these are quite regular there is some fluctuation the structure ation

22:11would be even smaller if I took a thousand count at a time

22:16this is just the law of large numbers i can never say what the time interval

22:21between single counts will be but i can say fairly accurately what the time

22:27interval for a large number of

22:29out will be well that's how we get the activity two plus from this graph now

22:36why does the activity decrease in this particular way for all radioactive

22:42substances this too

22:46it's the idea that disintegrations are random events perhaps I can simulate

22:51this behavior with a sort of game here are 60 dice think of them as Adams a

23:03rather small sample compared to the vast numbers of atoms in any piece of

23:07radioactive material

23:12suppose that the five represent atoms that have just disintegrated

23:20I'll pile them up here the test of the five turning up it's just the same as

23:32any other number it happens at random and is independent of what comes up on

23:37any of the other dice

23:47these represent the activity in the time interval of the first throw

23:54there are no longer the same atoms they were before they disintegrated so that

23:57they are eliminated from now on

23:59now i'll throw again the chance of any one of these dates coming out five is

24:19exactly the same as it was on the last row i'm calling these up besides the

24:30first row this time there are fewer Adams disintegrating is one more

24:43now i'm going to go on doing this thro after throw and you'll see what I get

25:01throw

25:15yeah

25:26I still have a few days left that i'm trying to get five West but i'll stop

25:45here this is a an activity time graph for this dice game and I want you to

25:52compare it with the activity graph for a radioactive substance the dice graph

26:04isn't smooth there are sizable fluctuations which are bound to occur

26:09because i had only a small number of dice but the general trend is exactly

26:14the same for both so it looks this the law of radioactive this integration is

26:20the same as the law of chance for these dice i mentioned the log test for the

26:25dice but I better say it again the chance of any one of the dice turning

26:31out five is exactly the same on every throw this means that the chance of an

26:37atom exploding in any one time interval is the same as in any other it doesn't

26:43change whatever as time goes on Adams unlike people do not have a greater test

26:49of disintegrating as they get older the chance always stays the same

26:56perhaps you can calculate yourself what's the half-life of these dice

27:00should be from this experiment looks as though it is about four throws

27:09so far we've used one particular natural phenomenon radioactivity to illustrate

27:15random events that's because the random nature of the individuals disintegration

27:21is apparent orderly behavior is observed for a radioactive substance only at the

27:28time for a large number of counts is used as a measure of activity now you

27:34observe order behavior in the measurements that you make for instance

27:39you measure light intensity with a light meter the needles doesn't jump around in

27:46an unpredictable way does the orderly behavior that you observe always arise

27:53because of random events this question can only be answered by doing

27:57experiments many experiments show that sometimes the order that we observe does

28:05have at the roots randomness which is not apparent but this isn't always true

28:11sometimes experimental results indicate some sort of order at the roots

28:18how can we tell when randomness underlies orderly behavior here is order

28:24the behavior every time these squares fun around the reading with eight it's

28:30clear that the order here comes from randomness but i could not tell by

28:35watching the meteor alone but this was true

28:39now suppose that I masked off all of the squares here except 16 the light going

28:46to the meter will decrease and I have to use the more sensitive meter but the

28:51point of this is that i could then tell by watching the meter alone that there

28:56was randomness it would be just like the machine with 16 squares there would be

29:01observable fluctuations you know that's very large numbers of photons arriving

29:08at a light meter produce the reading

29:13is the arrival of a photon a random event to tell us it would be necessary

29:18to cut the number of photons arriving at the meter down to a much smaller number

29:24and of course use a much more sensitive detective experiments like this have

29:30been done this film shows an oscilloscope which is connected to a

29:35very sensitive light detector you can see the pips caused by the arrival of

29:42individual photons and you can see from the intervals between them that there is

29:47evidence of randomness we see border in the world around us order that enables

29:55us to make measurements for instance measurement of light intensity with a

30:00light meter often this order arises from random events such large numbers of

30:08random events that the most probable thing is the thing we always observe

30:16yeah

30:21yeah

Published on Oct 6, 2015