It flips backwards.

I'll number the flips.

P of flips because that's the only way you can get the flips.

This is my flips column.

This sign flips that around.

Multiple flips, a little more time.

Sorry, I'm picking four of exactly six of the flips to end up heads, right?

Now. Let's go to 5 coin flips.

But if I have 8 flips there's 56 ways of picking 3 of those flips to be heads.

Does the probability of every given proportion, for example, one head out of 100 flips, 10 heads out of 100 flips or 1500 heads out of a billion flips becomes small.

Specify the amount of time between card flips

So he unties Odysseus, and Odysseus flips out.

Together they are equal to P of flips.

Out of 5 rolls, out of 5 flips.

And another chap flips his wings! All right.

It could be any 1 of those 5 flips.

Let's give a name to each of our flips.

And you can see, eventually the airport actually flips directions.

And what this theorem really is about many coin flips.

This is going to close down. This arrow flips around.

To make it easier, assume only a 3 coin flips and ask the probability of heads coming up exactly once.

And the way you could think of it is we're going to do 5 flips, exactly 1 of the flips is going to be heads.

So similarly, if I'm just picking flips and I have to say, OK, 3 of these flips are going to get into the heads car.

Let me ask you again. Suppose you make 111 coin flips.

So we talked about coin flips, and we flipped some coins.

Suppose I care about 90 confidence and from my coin flips,

As a result it could really happen that this thing flips.

Andrew flips them around and inserts them back in the body

I'm choosing which of the flips get selected, so to speak.

So let's ask an easy quiz. Suppose we do 2 coin flips.

Perhaps the picture completely flips if you are low on socioeconomic status.

So depending on where it looks, it flips between the two possibilities.

So it kind of flips things on its head a little bit.

And then essentially we would multiply 2 times the number of flips.

So let me make a little column, we'll call these the flips.

So it's essentially, there are going to be out of six flips,

We know that the variance of each of those individual coin flips is 0.25, but I want to add up all the outcomes.

The way she flips her ponytail will charm you before you realise it.

But when you fold it that way again, it, like, flips inside out.

These are the empirical means for these sequences of a 1000 coin flips.

Where you get out of the six flips, you're essentially choosing four heads.

So probability is a method of describing the anticipated outcome of these coin flips.

It applies to coin flips and many of the other examples we discussed before.

So we now understand that certain coin flips are dependent on other coin flips, so if god, for example, flips the coin of us having a disease or not, then the medical test again has a random outcome, but its probability really depends on whether we have the disease or not.

Well, you might vaguely remember that the sum of two coin flips or sum of any two of anythings you do, the variances just add up.