Because it is a little bit of a game.

That it's the probability of the data given the null.

If the observation, however, is not in line with your prediction, then the hypothesis is rejected, and if the hypothesis is rejected, you need to come up with a new hypothesis, and this cycle going along from hypothesis through prediction through experiment through observation and gradually refining the hypothesis or coming up with alternatives.

So now, we have two hypotheses and we'd like to test them.

But that assumption was a problem.

less a flat, constant straight line.

Or the truthiness is out there. Since this is all probabilistic.

Now, for the answers. A theory is the outcome of the scientific method.

And you can't do a reverse inference here. You can't say that.

A possible explanation for what you just observed.

It's not the probability that the Null hypothesis, of the Null hypothesis being true.

For bugs, we do not.

And this pair of parameters corresponds to that hypothesis, corresponds to flat line, that is, h(x) equals 360 plus zero times x.

Now in practice there's actually a trade off between a really good data fit and low complexity.

It's pretty much what you'd expect.

I have my own ideas about what could be at the heart of this, but I'm wide open to others.

It's a hypothesis,

That's a lot of whatifs.

And our null hypothesis is equals p 0.9 and our alternate hypothesis, if it doesn't work, as well as the null hypothesis, so p is smaller than 0.9.

Right now, this is all supposition.

That is another n plus 1 dimensional vector.

Now there is a new market and here, the customers are really unknown.

How will we go about testing the hypothesis

Try to drill into my students at Princeton is don't make that mistake.

Conversely, if the null hypothesis falls outside, you don't believe null hypothesis and you accept the alternate hypothesis.

Does every French utterance have an equivalent Japanese utterance?

It could actually be 90 as advertised and you were just unlucky, but we believe that high probability of this is correct or is the data actually contradicting this claim and you should complain that the claim is incorrect.

I'll explain using photographs.

That is, the diagnosis was valid from the beginning on, and therefore, was a correct explanation of how the failure came to be.

I'm going to interpret my hypothesis as follows.

And you the experimenter have to decide which is right.

So let's do it. To recap, we have sample 11 yeses and 4 nos.

With the statement, we can now go and check the value of tag all through the loop.

You're going to be living this for the next couple of weeks if you're doing this for real.

There are also many ways to refine a hypothesis and reject a hypothesis.

So, same as last time, we want to understand the hypothesis H and the cost function J. So, here's my training set of housing prices and let's make some hypothesis.

That's the point.

We'll do one segment that's the bare bones basic introduction to regression.

And I created a little oil spill in the Gulf of Mexico.

So what scientists do is they have a hypothesis that they think might be likely to start with.

Of course if you retain and the null is in fact true, then great you made the correct decision.

If this is our hypothesis, we now must devise an experiment to further refine this hypothesis.

I In theory, yes.

So that's a mouthful but let's say it again.

So the physics is that you cannot prove a hypothesis, but it's easy to disprove it any hypothesis is shaky.