# Introduction to experiment design | Study design | AP Statistics | Khan Academy

– [Instructor] So let’s say
that I am a drug company and I have come up with
a medicine that I think will help folks with
diabetes, and in particular, I think it will help reduce
their hemoglobin A1c levels, and for those of you who aren’t familiar with what hemoglobin
A1c is, I encourage you, we have a video on that on Khan Academy, but the general idea is if
you have high blood sugar over roughly a three-month period of time, high blood sugar, and I can
say high average blood sugar, you’re going to have a high
A1c, a high hemoglobin A1c level and if you have a low average blood sugar over roughly a three-month
time, you’re going to have a lower hemoglobin A1c. So if taking the pill
seems to lower folks’ A1c levels more than is likely to happen due to randomly or due to other variables, well then that means that your new pill might be effective at
controlling folks’ diabetes. So in this situation, when
we’re constructing an experiment to test this, we would
say that whether or not you are taking the pill, this
is the explanatory variable. Explanatory variable, and the
thing that it is affecting, the thing that you’re
hoping has some response, in this case the A1c
levels are your indicator of whether it is help
controlling the blood sugar, we call that the response variable. That right over there is
the response variable. So how are we actually going
to conduct this experiment? Well let’s say that we
have a group of folks, let’s say that we have been
given a group of 100 folks who need to control their diabetes. So 100 people here who need
to control their diabetes, and we say, “Alright, well
let’s take half of this group “and put them into, I guess
you could say a treatment group “and another half and put
them into a control group “and see if the treatment
group, the one that actually “gets my pill is going to
improve their A1c levels in a way “that seems like it would
not be just random chance.” So let’s do that, so we’re
going to have a control group, so this is my control
group, control, and this is the treatment group,
this is the treatment group. And you might say, “Okay,
we’ll just give these folks, “the treatment group the
pill and then we won’t give “the pill that I created
to the control group.” But that might introduce
a psychological aspect that maybe the benefit of the
pill is just people feeling, “Hey I’m taking something
that’ll control my diabetes,” maybe that psychologically
affects their blood sugar in some way and this is actually possible, maybe it makes them act
healthier in certain ways, maybe that makes them act
unhealthier in certain ways ’cause they’re like, “Oh
I have a pill to control “my diabetes, my blood sugar, I can go eat “more sweets now and it’ll control it.” And so to avoid that, in
order for just the very fact that someone says, “Hey I
think I’m taking a medicine, “I might behave in a
different way or it might even “psychologically affect
my body in a certain way,” what we wanna do is
give both groups a pill, and we wanna do it in a
way that neither group knows which pill they’re getting. So what we would do here
is we would give this group a placebo, a placebo, and this group would actually get the
medicine, the medicine, but those pills should look
the same, and people should not know which group they
are in and that is a, when we do that, that is a
blind experiment, experiment. Now you might have heard about
double-blind experiments. Well that would be the case
where not only do people not know which group
they’re in, but even their physician or the person who’s
administering the experiment, they don’t know which one they’re giving, they don’t know if
they’re giving the placebo or the actual medicine to the group. So let’s say we wanna do that. So we could do double,
double-blind experiment, so even the person giving the pill doesn’t know which pill they’re giving. And you might say, “Well
why is that important?” Well if the physician knows,
or the person administering or interfacing with the
patient, they might give a tell somehow, they might not
put as much emphasis on the importance of taking
the pill if it’s a placebo, they might by accident give
away some type of information. So to avoid that type of thing happening, you could do a double-blind,
and there’s even, some people talk about a
triple-blind experiment where even the people analyzing the data don’t know which group
was the control group and which group was the treatment group, and once again, that’s
another way to avoid bias. So now that we’ve kinda
figured out, we have a control group, we have a treatment
group, we’re using A1c as our response variable, so
we would wanna measure folks’ A1c levels, their hemoglobin
A1c levels before they get either the placebo or the medicine and then maybe after three months, we would measure their A1c after, but the next question is, how
do you divvy these 100 people up into these two groups,
and you might say, “Well I would wanna do it
randomly,” and you would be right ’cause if you didn’t do
it randomly, if you put all the men here and all the women here, well that might, first of
all, sex might explain it or behavior of men versus
women might explain the differences or the
non-differences you see in A1c level, if you get
a lot of people of one age or one part of the country
or one type of dietary habit, you don’t want that, so in order to avoid having an imbalance of some
of those lurking variables, you would want to randomly
sample and we’ve done multiple videos already on ways to randomly sample, so you’re going to randomly sample and put people into either groups. And a very simple way of
doing that, you could give everyone here a number from one to 100, use a random number generator to do that and then, or you could use
a random number generator, pick 50 names to put in the control group or 50 names to put in the treatment group and then everyone else gets
put in the other group. Now, to avoid a situation,
just randomly by doing a random sample, you might
have a situation where there’s some probability
that you disproportionately have more men in one group or
more women in another group and to avoid that, you
could do really a version of stratified sampling
that we’ve talked about in other videos, which is
you could do what’s called a block design for your random assignment where you actually split
everyone into men and women and it might be 50-50 or it might even be just randomly here you got
60 women, 60 women and 40 men and what you do here is you
say, “Okay let’s randomly “take 30 of these women and
put ’em in the control group “and 30 of the women and put
’em in the treatment group “and let’s put randomly 20 of
the men in the control group “and 20 of the men in the treatment group” and that way someone’s sex is less likely to introduce bias into
what actually happens here, so once again, doing this
is called a block design, really a version of stratified sampling. Block design, and there might
be other lurking variables that you wanna make sure
doesn’t just show up here randomly and so you might want, there’s other ways of randomly assigning. Now once you do this, you
see what was a change in A1c. If you see that, hey, the change in A1c, one if you see there’s no
difference in A1c levels between these two groups, and you’re like, “Hey, there’s a good probability
that my pill does nothing” and once again it’s all
that you’re just unlucky and it might be a very
small chance and that’s why you wanna do this with
a good number of people and as we forward our
statistics understandings, we will better understand
at what threshold levels do we think the probability
is high or low enough for us to really feel
good about our findings. But let’s say that you
do see an improvement, you need to think about,
is that improvement, could that have happened
due to random chance or is it very unlikely that that happened due purely to random chance,
and if it was very unlikely that it happened due
purely to random chance, then you would feel pretty good, and other people when
you publish the results, would feel pretty good
about your medicine. Now, even then, science is not done. No one will say that they are 100% sure that your medicine is good,
there still might have been some lurking variables that we
did not, that our experiment did not properly adjust
for, that just when we even did this block design, we might have disproportionately
gotten randomly older people in one of the groups
or the other or people from one part of the country
in one group or another so there’s always things to think about and the most important
thing to think about, even if you did this as good as you could, you still, some random
chance might have given you a false positive, you got
good results even though it was random, or a false
negative, you got bad results even though it was actually random. And so a very important
idea in experiments and this is in science in
general is that this experiment, you should document it
well and it should be, the process of replication,
other people should be able to replicate this experiment and hopefully get consistent results so it’s not just about the results, it’s your experiment
design, other people should, it should be an experiment
that other people could and should replicate to reinforce the idea that your results are actually true and not just random or just due to some bad administration of
the actual experiment.

## 8 thoughts on “Introduction to experiment design | Study design | AP Statistics | Khan Academy”

1. ChloePanda 1011 says:

First view and comment

2. Brandon SpaceHero says:

Aww man!

Second

Let's go

5. BritneyFreak34812 says:

Everytime I find a Khan Academy video and hear Sal.. I already know he's just going to repeat sentences constantly

6. Len Leib says:

This looks like it might be good for an AP Statistics Class ðŸ™‚

7. Michelle Nasrudin says:

can you get this lecture in Swedish in any way? would be so good.

8. Bryan Quidlat says:

Thanks mate, it really helped a lot ðŸ˜€