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Video transcript

what we are going to do in this video is talk a little bit about experiments in science and experiments are really the heart of all scientific progress if you think about let's just say this represents just baseline knowledge and then people have hunches in the world and for a lot of times people say hey I have a hunch that that thing is good for you or that thing is good for you but they really had no way of measuring how confident they were there were no they really had no good way of proving it and even more because they had no good way of proving it it was hard for people to build on top of that knowledge but with the scientific method and experiments people were able to say hey we have a hypothesis here and we were able to do some well-designed experiments and so we feel pretty good that this is true and then future people are going to say hey since we feel pretty good that this is true maybe we can design an experiment to see whether that is true hey that actually is true and then they can build on that and we end up having scientific progress that can accumulate over hundreds of years and this is really important that the experiments are well designed because in the future and this happens all the time we might realize that hey actually there was a little a few assumptions baked in here that weren't accurate that allowed us to make essentially misleading conclusions so our conclusion wasn't quite right there and then we all have to rebuild from that point in order to make sure that we are truly making progress so the key question is how do we set up well-designed experiments and it's a whole field of study but the whole purpose of this video is to really give an introduction to it so let's just start with a hypothesis let's say that you have a hypothesis that some pill that is made up of the the petals of some flower that this pill right over here it improves it improves running running speed it improves running speed if someone were to take it so the important thing of any hypothesis it has to be testable and so what you do is you have to think well how am I going to test it well what can do so how are you going to test your hypothesis at first you might say give the bill so give the pill to some some runners to some runners and test their time test their 100-meter time test their 100-meter time before the pill before the pill and after and you might say hey maybe if you know I don't know there are times improve after maybe my hypothesis is correct pause this video and see if you feel comfortable with with this test right over here this experiment well actually there's several problems with this experiment how are you selecting these or runners and if you give them the pill and their speed improves did it truly improve because of the pill or did it improve because of some other thing that they are doing maybe they got new shoes or maybe their diet improved in some way or maybe they just had a psychological improvement this is often known as the placebo effect if people are taking something that they think will believe that they think will help them it often will help them even if that thing is just a just an empty capsule or just a sugar tablet so how do you avoid these types of errors well what you could do is you can find runners and put them into two groups so let's say this is one group right over here and then this is another group and what you would want to do is you'd want to go into the population of people and you would want to randomly select whether someone goes into one group or another group why random because if you don't randomly select there's a chance that there might be some implicit bias that you might just happen to be picking people who maybe their running speed is on an upward trajectory and they just happen to go into the group that will eventually get the pill so you randomly randomly put them in those groups and what you want to do is you'll have a control group and you'll have a group that gets your pill and so this group gets the pill gets the pill and now you might be tempted to for this group to say well they don't get a pill and then after a few months of it and it should be the same amount of time you say hey did this groups times improve over the hundred meters how did that compare to this group but be very careful if this group gets the pill and this group gets nothing then the pill might be providing that placebo effect again that just making people think they're getting something that's making a faster might actually be a self-fulfilling prophecy so it's actually important that you also give these people a pill although this pill would just look like a pill so this would be just an empty empty empty pill that looks the same now there's another idea when you're designing scientific experiments then it needs to be double-blind let me write this down double-blind so as you can imagine it implies that two things are blind here so the first thing that needs to be blind is the people themselves should not know which group they're getting put into they should not know which pill they are taken because obviously if you put someone in this pill in this group and you say hey you're in the control group we're just going to give you an empty pill well then the placebo effect might not be in might not work it's also important because it's double-blind that the people who are working with the runners the people who are measuring them so that the researchers right over here so I'll draw someone with a clipboard so the researchers who are observing these people and maybe administering the pill and telling them the about the experiment that they too do not know which group they're administering it to because if they did they might be able to signal somehow they might be able to even subconsciously give a sense of which group folks are in and so let's say we do all of these things and so we're getting in the direction of a well-designed experiment and we find that the ten people in this group versus the ten people in this group after three months these folks had a 5% improvement in running speed and these people had a 10% improvement in running speed is that enough to conclude that our hypothesis is correct well you might be tempted it seems suggestive but that's where statistics come into order because there's just some random chance that you got lucky that you happen to pick the people that your pill does nothing but you just happen to pick the people who are going to improve more and there's a whole field of inferential statistics when you take a statistics course that will go in more depth into this but essentially what you're gonna do is you're gonna say hey assume that your pill does nothing what's the probability of getting this result for 10 people or what's the probability of getting this difference in result and if that probability is very low well you say hey that would suggest that my pill actually does do something now another important principle of a of an experiment like this is it needs to be replicable replicable because even though you thought you did a good job people might not want to take your word for it and it's important in science for people to be skeptical when people do experiments they want to have a result and that bias might creep in and so if someone else does an experiment you need to say how you did that experiment so other people can see if they get the same results because even though you think you randomly selected you might only do it with people from a certain country or under certain weather conditions or assuming other constraints and then these people might do it slightly differently or in a different country or under different constraints and realize that hey the explanation for this maybe was something else another thing to keep in mind is the larger your samples right over here the larger groups that you're able to do this with the stronger that the statistics actually be actually become and I would say not just larger but the more diverse across genders across ethnicities across geographies so the big picture here is is all scientific progress is based on us designing good experiments and being very rigorous about how we think about those experiments and what I've highlighted here is just the beginning of how we might think about designing those experiments and as you go into your scientific careers look at other people's experiments and see whether they've done these things because many times you will find that it is not as rigorous as it might seem at first
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