If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

Main content
Current time:0:00Total duration:5:37
AP.STATS:
VAR‑3 (EU)
,
VAR‑3.C (LO)
,
VAR‑3.C.9 (EK)
CCSS.Math:

Video transcript

the last video we constructed an experiment where we had a drug that we thought might help control people's blood sugar we looked for something that we could measure as an indicator for their blood Sugar's being controlled and hemoglobin a1c is actually what people measure in a blood test it is we have a whole video on it on Khan Academy but it is a average measure of your blood sugar over roughly at three-month period so that's the explanatory variable whether or not you're taking the pill and the response variable is well what does it do to your hemoglobin a1c and we constructed a somewhat classic experiment where we had a control group and a treatment group and we randomly assigned folks in to either the control or the treatment group and to ensure that one group or the other or I guess both of them don't end up with an imbalance of in the case in the case of the last video an imbalance of men or women we did what we call a block design where we took our hundred people and we just happen to have 60 women and 40 men we said okay well let's split the 60 women randomly between the two groups and let's split the 40 men between these two groups so that we have at least an even distribution with respect to sex and so we would measure folks a1cs before they get the treatment or the placebo then we would wait three months of getting either the treatment or the placebo and then we'll see if there's a statistically significant improvement now this was a pretty good and it's a bit of a classic experimental design we would also do it so that the patients don't know which one they're getting placebo or the actual treatment so it's a blind experiment and it's probably good if even the nurses or the doctors who are administering the pills we're giving the pills also don't know which one they're giving so it would be a double-blind experiment but this doesn't mean that it's a perfect experiment and this seldom is a perfect experiment and that's why it should be able to be replicated other people should try to prove the same thing it may be in different ways but even the way that we designed it there's still a possibility that there are some lurking variables in here maybe you know we we took we took care to make sure that our distribution of men and women was roughly even across both of these groups but maybe by through that random sampling we got a disproportionate number of young people in the treatment group and maybe young people responded better to taking a pill maybe that you know that it changes their behaviors in other ways or maybe older people when they take a pill they decide to eat worse because they say oh this pill is going to solve all my problems and so you could have these other looking variables like age or where in the country they live or other types of things that just by the random process might you might have things get uneven in one way or another now one technique to help control for this a little bit and I shouldn't use the word control too much another technique to help mitigate this is something called matched pairs design matched matched pairs pairs design of an experiment and it's essentially instead of you know going through all of this trouble saying oh boy maybe we do block design all this random sampling instead you randomly put people first into either the control of the treatment group and then we do another round you measure and they do another round where you switch where the people who are in the treatment go to the control and the people who are the control go into the treatment so we could even extend from what we have here we can imagine a world where the first three months we we have the 50 people in the this treatment group we have another 50 people in this control group that are taking the placebo we see what happens to the a1cs and then we switch where where this group over here then and they don't know they don't know first of all ideally it's a blind experiment so they don't even know they were in the treatment groups and hopefully the pills look identical so now that same group for the next three months is now going to be the control group and so they got the medicine for the first three months and we saw what happens to their a1c and now they're going to get the placebo they are going to get the placebo for the second three months and then we are going to see what happens to their a1c and likewise the other group is going to be switched around the thing that the folks that used to be getting the placebo could now get could now get the treatment they are now going to get the treatment and the value here is is that because everyone is going through but is that for one period is in the control group and for one period is in the treatment group and they don't know when which one is happening you are less likely to have a lurking variable like age or geographic region or behavior that cause an imbalance or in some how skew the results or give you biased results so this is an interesting thing and and you know even what I've talked about in this video in the last one these are just different ways to approach it and as you construct experiments this is in medicine you'll obviously construct experiments in other fields it's important to think about what types of things are practical to do and also are have the best chance at giving you real I guess you say an unbiased and real information as to in the case of an experiment to the efficacy of something or whether a certain variable an explanatory variable really does a drive have a have a causal effect on a response variable
AP® is a registered trademark of the College Board, which has not reviewed this resource.