BAM!
Mr. Tarrou.
Ok.
We are going to finish our discussion about experimental designs in Statistics.
And with this video we are going to go over the different experimental designs we have.
We have got Block Design, Matched Pairs Design, and Double Blind Experiments.
Ok.
You Block by a variable that you feel will have an effect on the response variable.
You are taking a bit more control over the experiment, not to be confused with a control
variable.
And a control, or in an experiment you control what you can, block what you cannot control,
and randomize the rest.
Ok.
So this sounds a lot like, and it is very very similar, to Stratified Random Samples.
Only stratifying is a language or a vocabulary word for sampling.
Blocking is for experiments.
We are about to do an outline of an experiment, so you have a scenario...
We will do medical studies again... you know...
Men have supposedly a lot of testosterone in their systems and women have a lot of estrogen.
So let's just use that as an excuse for a particular medicine that we are studying.
We think..
ok..
I can't of course control somebody's gender and I also think that the gender.. that sex...
is going to effect the way the chemical, the drug that I am working with or herb or whatever
I am studying, I feel like it might be a possibility that it works differently between men and
women.
Whether that is a hunch, or whether it is based on past data or experience where we
have had a lot of medicines act differently for men and women..
whatever that may be, you strongly feel like this variable of gender is going to effect
the results or the way that we respond.
And maybe cloud the issue...
Be a lurking variable for how it is, the response is working with the explanatory variable.
So if that is the case, we will Block.
So let's just say that we have 500 individuals that have offered to be in a medical study.
And in that medical study we feel like, again gender is going to make a difference in how
the body responds to the drug.
So within this 500, I just happen to have the same number, it does not have to be the
same number, but we are going to just for education purposes in this example say that
there are 250 men and 250 women.
So we are going to take the 500 individuals that we have and knowing that we believe that
gender will effect the results, we have a selection or a group of 500 individuals split
evenly between the two genders.
Now I said here not to be confused with Control groups.
The men are not going to act as a Control group to the females.
No no no.
Both of these blocks are going to get both treatments.
You can have more than two treatments.
You can have 3, or 4, or 5 treatments or more.
And then just one of them being the control group.
In this case, I am going to outline.. which is sort of like short hand for a description
of an experiment...
this blocking design.
so we have a group of individuals.
There are 500.
We blocked them into men and women.
They don't have to be the same number, but in reality the closer you can get these groups
the easier they are for comparison...
at least I believe so.
We are going to then, within each block, apply the treatment and the control.
So let's say that we are going to have one group get the drug and one group get the placebo
of whatever this generic example is about.
And then again drug, or just you know whatever the treatment may be, and placebo.
You know maybe we want to effect the...
I don't know... see the effect of taking vitamins.
So maybe someone is actually taking a vitamin and someone is just taking a sugar pill or
whatever they put into placebos!
And we will mark how many people are going to be in each of these treatment groups.
And for illustrative purposes and with your treatment and the control groups, the more
even those are in size the better, so out of these 250 people I am going to indicate
with my outline that 125 are going to get the drug and 125 the placebo within the groups
of men and women.
Now the purpose of an experiment of course is to compare the results.
So we are going to compare whatever the response variable is that we are concerned with between
the treatment and the control group.
And then of course we did block by gender because we thought that they might possibly
respond differently to whatever this treatment is to begin with.
So we are not going to just compare wishing the men and do a comparison for the females.
But, we are also going to do a comparison between the gender.
And see if my hunch was correct, that the sex of the patient is going to influence the
response to the drug.
Two comparisons: within each block, and finally between whatever variable it is we blocked
by.
Now every experiment or sampling must have some form of randomization.
Now I have students all of the time say, with an outline by the way all you have to do is
say I am going to randomize..
You don't have to describe how that is done.
Not with an outline, but you will with a description of an experiment.
I have had a number of students tell me things like when they go to block, that they will
randomly assign the gender or the race of people, or whatever...
somebodies age... or whatever the unit is that you are working with.
[laughing] You cannot randomize somebodies gender.
So that would not make any sense.
But you will randomize what treatment they get, or if they get the control.
That is where the randomization would be or how you would state the randomization in an
outline.
Blocking, BAM!
Alright.
[nananananana] Oh!
Another purpose of Blocking, is when you create groups that are more similar then you are
taking more control and within those groups that are more similar, where all the men are
together or all the women are together, if that truly effects the way the drug behaves
you will create a test that is more sensitive.
You will minimize the variability of the results.
And the same thing is true is matched pairs design.
You give two treatments to two closely matched subjects, or the same subject.
So... um... if I am doing a sports test between water and Gatorade, and I want to test if
that effects the recovery rates maybe I will put two players together that I know that
are the same age... have the same based on resting heart rate, I am not a doctor, but
have the same physical fitness, and maybe if we are doing sprints have the same speed,
and whatever 50 or 100 dash sprint that we are talking about.
Then make those two exercise together, but give one the water and one the Gatorade.
That would be like 2 closely matched subjects.
Or very often with matched pairs though you are doing two treatments on the same unit
or the same subject.
Like if it is a dexterity test you have somebody do something left handed then right handed.
Or if it is Pre and Post test.
That is kind of like two treatments where the same person will take a Pre and Post test
and that is like your two treatments on the same subject.
Let's see...
Maybe you have plots of land and those plots of land have something, they are very similar,
so within plot of land you put two different types of trees.
There is are lots of examples and most Matched Pairs designed experiments will be doing two
treatments on the same subject.
But it can be two closely matched subjects.
The purpose of Blocking is to minimize as many variables as possible and isolate the
effects of the treatment.
making the experiment more sensitive.
Making it easier to determine if the treatment is actually having an affect on the response
variable You are either randomizing which of the 2 matched subjects get the treatment,
or when you have the same unit or subject getting both treatments you need to randomize
which of the two treatments come first.
So in a dexterity test, if you are having somebody doing something left handed and then
right handed...
Well I am right handed.
So most things I do better with my right hand.
But even if you have me do a test with my left hand first I will learn something about
what it takes to get that task done which might make my right hand work even faster.
So when you do that experiment, you know, you would randomize.... ok... half of the
subjects by coin flip or random selection... or names out of a hat... but half of the subjects
or units will do something with their left hand first and the other half will do the
dexterity test with their right hand first.
That kind of idea where you are randomizing which treatment they get done first.
In your textbook a lot of Matched Pairs Design questions, these tend to be at the end of
the chapters.
Like when we get to studying One Sample Mean z and t tests...
You don't know what that means yet.
But these matched pair design questions will show up at the end of those sections and they
will look like there are two samples, where there is a column of numbers..
Actually excuse me, two columns of numbers.
Well because we are dealing with those in pairs, we will actually be working with a
column of differences.
And you only analyze the difference between those two measurements...
like a pre and post test... like a speed of doing something with your left hand minus
the speed of something done with the right hand.
So these matched pairs design in the textbook tend to look like they might possibly two
sample tests, but indeed we are only working with the difference between those matched
values.
So it is a one variable test.
That does not mean much right now though in our textbooks.
So I will just mention later on that I have talked about that briefly..
back when we were doing the chapter about designing experiments.
It is matched pairs design.
Last type.
[nanananana] Ok.
Bam!
Alright.
Double Blind experiment.
Neither the subject nor the administer knows who is getting what treatment.
Use generic labels for the treatment and let the third party administer that experiment.
They return the results and you examine the results.
Now you as the designer of the experiment of course knows...
Let's say..
how do you even do a double blind experiment.
Medical study.
You design an experiment.
You want to test some kind of heart medicine maybe.
That is a critical type of setting.
You don't want to give anybody just a sugar pill when they have a life threatening illness.
But there are these two drugs that both treat the same illness.
Well, the doctors will just know that here is a drug A and here is drug B. They don't
actually know what treatment they are giving to the patient.
Maybe they will not be inclined to give the newer possibly better drug to the worse patients.
Well that is ok because both drugs will treat whatever that illness is.
You are just going to study if one drug does a better job.
So you are sending those drugs out.
They will labelled A and B.
The doctors supposedly won't know what they are administering.
The patients won't know what drug they are receiving.
And then at the end, whatever the health response is and the results will come back to you.
They will say, ok for drug A we got this response, for drug B we got this response.
Then you as the statistician will know what treatment A was, what treatment B was, determine
the probably of the observed difference or more and see if it is statically significant.
Then decide if the new drug was better or not.
There...
As long as you have designed a test, made generic labels, and then sent them out to
a third party to actually run the experiment it is not a big deal to run a double blind
experiment.
And their purpose is to help eliminate bias.
If it is an experimental design... if it is health study the doctors won't be inclined
to give the new medicine to the worst patients.
If you are just testing water proofing on shoes, maybe you won't use them differently
because you know you have the new treatment and not the old treatment.
And then finally experiments must have realism so that the results can be generalized to
the real world.
And that is the end of my discussion about experiments and being able to create good
data that we then analyze later with our statistical techniques.
I am Mr. Tarrou.
Thank you for watching.
Read your book.
It is really important to read the examples in this chapter.
And go do your homework.