Run Randomized Slide Show

This was a randomized trial.

So experimentalists use randomized experiments.

Similarly, using a randomized control trial in rural Pakistan,

learning. Randomized decision trees, another technique from machine learning.

Last segment we talked about the power of randomized experiments.

like, and it almost looks like a randomized experiment, right?

It was a randomized control study published in The Lancet.

So in this first segment, let's talk about randomized experiments.

Now we know they are effective because of randomized experiments.

Is again to drive home the power of those randomized experiments.

So we'll start out with two examples of randomized controlled experiments.

And you can do the same randomized, controlled trial for social policy.

So that's why these observational studies aren't as powerful as randomized experiments.

The first, I'll give you a couple of examples of randomized experiments.

Novocure conducted its first phase three randomized trial in patients with recurrent GBM.

Randomized clinical trials were actually invented in 1948 to help invent the drugs that cured tuberculosis, and those are important things, don't get me wrong.

But they lack the power of randomized experiments, and I'd like to show you.

Without randomized experiments, without random assignment to conditions, we can never really be sure.

So what we did is we did a randomized, controlled trial in 134 villages in Udaipur Districts.

In a randomized experiment, or whether we're just dealing with, observational studies, and the analysis of correlations.

Intelligence is a good thing. So in these randomized experiments, the population is just all healthy adults.

But I've seen it say False. The reason is it's a randomized algorithm. It's a particle filter.

Hi. Welcome to Statistics One. This is the first lecture where we'll talk about randomized experiments and observational studies.

The major benefit of randomized experiments is they allow us for, strong claims or strong arguments about causal mechanisms.

But remember, go back to lecture one, where we made the distinction between randomized controlled experiments and observational studies.

Um, but remember, back to Lecture 1, when we talked about randomized experiments, this is not a true independent variable.

The power of randomized experiments is they allow for the strongest arguments or strongest claims we can possibly make about causality.

Again, the problem here is it was difficult to, difficult to do a true randomized experiment ah, especially in the '60s.

Then you have to do things like grid search or a kin of particle filters as randomized methods for searching as well.

Because a lot of areas in, in the social sciences, they have to rely on these observational studies. They, they can't do randomized experiments.

So what I'm going to do in this first lecture is just talk about the difference between a randomized experiment and an observational study.

And in the 20th century, randomized, controlled trials have revolutionized medicine by allowing us to distinguish between drugs that work and drugs that don't work.

They had gotten their chemotherapy, and then they randomized them, where half got placebo, and half got a drug called Zoledronic acid that builds bone.

It doesn't solve the TopK problem better than the randomized algorithm we talked about the partitioning algorithm but it does it pretty fast and there's other uses for this.

And they were. They were prepared.

People were suffering. People were fighting.

Teachers were prisoners. Teachers were volunteers.

They were focused. They were engaged.

This is kind of a different idea but I wanted to introduce the idea of a randomized algorithm and we did that a little bit in the context of the expected

They were not eaten, they were thrown away because they were dead.

There were few passengers who were safe.

We were poor, but we were happy.

The bedrooms were cold. They were unheated.

Classes were fixed, so were the breaks.