So we ask you to think about get, keep and grow.

I need to compare the numbers, confirm my suspicions.

You're going to test the problem.

But you can't guess forever.

And so the phases of customer discovery start with stating your hypotheses, testing the problem, testing the solution, or verifying or pivoting.

At this point, we have systematically fixed the first bug using the scientific method to systematically come up with a hypothesis and refine it, refine it again, possibly come up with alternatives until we end up with a diagnosis.

And just in summary, no matter how far I push this, I can't get it to break.

What you can do is call a Sobel test, which tests the null hypothesis that the indirect effect is zero.

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

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

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.

The facts bear out this hypothesis.

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.

looked like this on day one to something that looked like this on day five.

And so in this case we reject the null hypothesis and therefore accept the alternative.

1.366 was the size of the interval, and we got a false. We don't believe h zero.

And, we'll get to power in the, in Lecture nine when we do some of these examples in

The second problem I mentioned is the idea of a binary outcome.

I certainly do all the time I want you to apply that same level of formal reasoning. Think about where the bug might be.

The last lecture, it will be all about how to do model comparisons.

Validation

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We checked with the experiments, validated the prediction.

What we're looking for is not just data, it's insights, and how this process will work is we'll get out of the building, and then as we find those new insights we'll actually change the canvas.

Now, I'd like to give you a number of situations where you might be able to use hypothesis tests.

It's a hypothesis,

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Suppose our technologies for finding connectomes actually work.

One approach that I'll describe here is

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

A new hypothesis is the error is due to the quote condition evaluating to true.

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