Statistics Between and Within Treatment Variability

Hi and welcome to www.educator.com.0000

Today we are going to be talking about between and within treatment variability.0002

Here is how we are going to do this.0006

First we are going to talk about all experiments have in common, just recap and then we are going to talk about 2 types of variability within experiments.0010

That is going to be between treatment variability and within treatment variability.0020

Then we are going to reframe the goal of all experiments in terms of variability.0025

We are going to talk about different types of random assignment or different types of control and that will help us control certain kind of variability.0031

Those three different kinds of random assignment IV are completely randomized design, matched pairs design, and repeated measures design.0041

Okay the first thing about experimental designs is that all of them have two things in common.0051

One is that they have manipulation and control of the treatment variable or we call the IV or the independent variable.0058

They all have that in common.0072

Now concretely what that really translates to is that each experimental unit, whatever that unit is, would be a rat or cell or a CD or states or a person.0074

Each experimental unit or case gets assigned to some treatment.0089

This rat gets treatment A, this rat gets treatment B.0097

Everything in this experiment should be alike except for the treatment.0105

Everything else about these rats or states or schools or people should be roughly similar except for the treatment.0112

That is how we isolate the effect of that treatment.0124

Given that experimental overview, there are really 2 kinds of variability that we need to think about.0129

One is what we call between treatment variability and you can think of it as here are two groups that have 2 different treatments A and B.0138

We know what is the between group variability?0150

How are these two treatments different from each other or three treatments or parts of a treatment?0152

This variability is hopefully caused by the treatment.0158

The impact of the treatment is making on these two different cases were experimental units and that is really caused by the treatment.0164

This is what we think of as the good kind of variability.0178

This is what variability we are interested in.0182

We are looking for this type of variability because if we have a lot of between treatment variability this means they are manipulated variable is quite important.0187

It makes an impact.0209

That is one kind of variability.0210

The other kind of variability is within treatment variability.0215

That means within an experimental unit they all have the same treatment.0218

It all rats that were exposed to the same hours of sleep.0224

These rats had different hours of sleep.0229

These rats that all have the same hours of sleep there a still some little differences between them.0230

Some rats sleep a little bit more or the other one is active.0239

They have a little bit of within treatment variability that will just come along for the ride.0241

Despite the rigorous experimental methodology, no matter how good your manipulation and control is0252

They were this some variability that will exists even within a treatment group and that could come from lots of different sources.0260

Sometimes it does come from experimental mistakes and sloppiness,0268

Most of the time it just comes from noise or other variables that we are not accounting for.0275

This is what we think of as bad variability, but is not as bad like we want to or if you have it, then oh your terrible experimenter.0281

That is not what it means.0294

It just means that this gets in the way of seeing the good kind of seeing the between treatment variability.0296

Here is the problem.0315

Within treatment variability is very difficult to avoid.0318

They are some techniques to try and avoid it and minimize in as much as possible and we are going to learn about those0323

Overall even though you try and reduce it probably never completely goes away.0330

Here is the issue with it.0337

It prevents us from seeing the between treatment variability because sometimes it obscure it.0338

Let me show you what that means in the next slide.0346

Because of these two different kinds of variability we want to update one of the goals of the experimental design.0354

Not only do we need to have manipulation and control of the IV in order to determine causality.0363

We are very interested in causality and experimenter so this is all in order to causality.0375

All so we want to know where as much as possible within treatment variability as much as we possibly can.0381

There can be a couple of different methods for lowering within treatment variability as much as we can.0400

Because that can help us see between treatment differences and that is where we are really interested in, in order to determine causality.0407

Let us look at an example to look at how this variability might impact each other.0419

Here is an example of drugs and driving.0424

Some antihistamines like allergy medicine make people feel drowsy like Benadryl.0428

One danger is that drowsy people are crappy drivers.0433

To check whether an antihistamine makes people worse at driving we wanted to test an antihistamine against the placebo,0438

Just a sugar pill on people's driving performance in a videogame.0445

Here is the ideal case.0449

In the ideal case in order to determine whether antihistamines cause people to be like crappy drivers.0454

One thing you might want to look at is let us say the score on the driving performance.0462

Higher score means you are better at driving lower score mean you have more like crashes or swerves, or whatever is right.0468

Maybe we will see if the we draw like a dot plot of the antihistamine people right.0475

Maybe we will see them all having sort of you know low scores but maybe the placebo group will just tend to get high scores.0483

If we had something like this then we will see that even though there is within treatment variability, there is variability here.0513

There are some within treatment variability here and there are some within treatment variability here too.0521

Basically the means are probably far apart enough where we could probably say all of the antihistamine seems to be different than the placebo group.0527

Between treatment difference is still big enough that we can see it.0540

But the world is not an ideal place.0547

Most of the time we have a sort of noisy world because some people are just better at driving games0551

and some are better at video game than and some people do not react to antihistamines as strongly as other people.0562

There are lots of other variables that come into play for within treatment variability.0568

We might have been no differences.0576

Maybe we can a lot of spread.0580

We have a lot of spread here and among the placebo group we might also have a whole lot of spread.0591

In the case is hard see if there is a between treatment difference just because there is a lot of within treatment difference.0602