If the hypothesis explains your observation, the hypothesis must be useful to make a prediction.

I made a hypothesis, a ridiculous hypothesis.

Make a test case that probes or falsifies that hypothesis.

So, as the experimenter, you'll make a decision to reject the null hypothesis or retain the null hypothesis.

logistic regression, I'm going to modify this a little bit, and make the hypothesis

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

I made a radical hypothesis.

You implicitly had a hypothesis.

That this Null Hypothesis is not a very competitive hypothesis sort of speed.

This is just a hypothesis but

AG Okay, that's a fourth hypothesis.

It's not the probability about a hypothesis, it's the probability about an observation given a hypothesis.

A theory is also particularly useful hypothesis because we refined the hypothesis until it becomes a theory.

This is a very exact to the setup you're going to study when we make hypothesis test.

There is a bit of a hypothesis though.

We cannot conceive science without a hypothesis.

Next hypothesis tests.

Next hypothesis complexity.

Look for the cancer hypothesis and the not cancer hypothesis.

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

From this initial observation, we derive a hypothesis.

I favored the hypothesis.

I favored the hypothesis.

So that's the hypothesis.

AG Okay, hypothesis two.

And the form of the hypothesis, which we use to make predictions, is this linear function.

Day 1, a series of untested hypothesis, a faith based enterprise.

The result confirmed my hypothesis.

This data supports the hypothesis.

Let's check our individual hypothesis.

But this is hypothesis generating.

And so our hypothesis is

Of this first hypothesis here.

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

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

So given a training set, we need to pick a value for the parameters theta and this hypothesis will then let us make predictions.

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.

like that one, this is not a particularly good hypothesis.

Here's the form of a hypothesis rewritten on the slide.

On day one, a series of untested hypothesis, a faith based enterprise.

Suppose the null hypothesis is 73, and with a two sided test, using a 95 confidence threshold would we accept or reject the null hypothesis?

The facts bear out this hypothesis.

These facts bear out my hypothesis.

AG Okay,there's his fifth hypothesis.

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.