You did all sorts of work putting that meal on your table.

What did your scientific tests show?

So just for our little Python program implementing the familiar expression of binomial distribution and with this program, I'll get the following.

For my first question, I'll assume P equals 1.

Let's see if it can add 1 1.

But it's not just the data.

Okay.

And now we'll use split to divide the quote into its words.

And that makes sense because we had a huge initial uncertainty, and inaudible of 5 with a relatively small measurement uncertainty.

The average score when I done it with the computer and the groups was 76

So 2 is less than 3, so we expect the result to be true.

Our experiment has revealed that his report was inaccurate.

There are certain off diagonal elements.

It actually turns out that there are actually 1 128 possible results that we can get, equally probable results.

Look at the outcome as blocks.

The result is still in doubt.

So 1, 2, 3, 4, 5, 6, and then the last time I get a tails.

Using our protocols... the results are conclusive.

If I run it, I construct an omega matrix piece by piece that's the one that you should come out with a xi vector, and then I run and print out, using the show command, the result of omega to the 1 times c.

Okay? (Laughter)

And here's my code, this implements Bayes rule.

Let's print russian(14, 11).

Then we graph the binomial just like before.

The result of his experiment corresponded with mine.

There's a probability for an outcome I'm going to call it A, for now.

Yes, that is part of testing.

That's a little easier to read.

So in order to confirm the hypothesis, we would expect an assert exception.

The flood is only visible at A and the stoplight only visible at B.

The probability of B is a sum over all probabilities of B conditional on A, lower caps a, times the probability of A equals lower caps a.

And now, let me say the probability of the test giving me a positive results a false positive result when there's no cancer is 0.2.

The autopsy data confirms what we already knew.

So if we look at this way there's this probability.

The ultrasound that I just showed her in there was not her ultrasound.

That is, go in somewhere over here where the last measurement is being applied and do the trick that I just showed you and see what the outcome is.

Would you like to close down the trial now, or should we wait until we have robust results?

It turned out that Justin was almost certainly not autistic.

We were disappointed with the results of the experiment.

Maybe it's sort of duncey all the way back.

From this hypothesis we have made predictions. We have made experiments.

Ma Yeah. So thrilling, definitely.

And we're going to apply the exact same mechanics as we did before.

And harnessed correctly, it can be a powerful force for good.

They needed openness.

Even more valuable, history provides a laboratory in which we see played out the actual, as well as the intended consequences of ideas.