Okay, in the next couple of videos we're going to introduce you to the exciting

topic of resample.

So many of you have probably taken a statistics class in the past, and

you did something called hypothesis testing.

Like you might try and test whether two populations have the same mean or

different means.

And there are so many tests.

There's the two sample z test.

There's two sample t test, matched pairs,

unequal variances, equal variances.

And I am sure most of you, including professors,

don't remember how these tests work.

Okay, hypothesis testing is really for the PhDs to remember this stuff.

But there is a really simple unified approach to circuit

testing hypothesis called resampling, okay?

And it doesn't make any assumptions.

Like, for instance, some of these tests assume populations are normally

distributed and we can use this in a couple of videos to analyze the flaking,

which has been a big sports and math problem.

Or we could use it, you'll see, in the next spreadsheet,

which may be this video or the following video, does the training

technique actually significantly improve the performance of a player?

Okay, well lets start with a non-sport example.

Let's supposed we have 12 people who tragically

have very advanced, let's say prostate cancer.

Okay, and so the old treatment, the per treatment,

only two of the six people survived and then we gave a a new drug, or

a new treatment to six people who really we think are identical in pretty

much how the cancer had spread and thankfully five out of six survived.

What is the probability that the new treatment is better than

the old treatment?

And you might recognize this as a test difference between proportions, and I mean

I don't want to do it that way because every test, I've gotta teach you a new set

of math, but resampling fits in well with simulation, and it's really simple.

Okay, here's what you do.