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## The idea of significance tests

Current time:0:00Total duration:7:49

# Idea behind hypothesis testing

## Video transcript

- [Voiceover] Let's say that
you have a cholesterol test, and you know, you somehow magically know that the probability that it is accurate, that it gives the correct
results is 99, 99%. You have a 99 out of
100 chance that any time you apply this test, that
it is going to be accurate. Now let's say that you, and
you just magically know that, we're just assuming that. Now let's just say that you
get 100 folks into this room and you apply this test
to all 100 of them. So apply, apply test 100 times. So what are some of the
possible outcomes here? Is it for sure that 99,
exactly 99 out of the 100 are going to be accurate
and that 1 out of the 100 is gonna be inaccurate? Well that's definitely
a likely possibility, but it's also possible
you get a little lucky and all 100 are accurate,
or you get a little unlucky and that 98 are accurate
and that two are inaccurate. And actually, I calculated the
probabilities ahead of time, and the goal of this video isn't to go into the probability
and combinatorics of it, but if you're curious about it, there's a lot of good
videos on probability and combinatorics on Khan Academy, but I calculated ahead of time, and the probability, if you have something that has a 99% chance of being accurate, and you apply it 100 times, the probability that it is accurate, that it is accurate 100
out of the 100 times, is approximately equal to, approximately equal to 36.6%. I rounded to the nearest
tenth of a percent. So it's a little better
than a third chance that you'll actually
get, all of the people are going to get an accurate result, even though for any one of them there's a 99% chance that it is accurate. Now we could keep going, the probability that it is accurate, I'm just gonna put these quotes here so I don't have to rewrite
accurate over and over again, the probability that it is
accurate 99 out of 100 times, I calculated it ahead of time,
it is approximately 37.0%. So this is what you would expect, getting 100 out of 100
doesn't seem that unlikely if each of the times you apply it has a 99% chance of being accurate, but it makes sense that
you would expect 99 out 100 to be even more likely,
slightly more likely. And we can of course keep going, the probability that it is accurate 98 out of 100 times is
approximately 18.5%. And I'm just gonna do a few more. The probability that it is accurate 97 out of 100 times, and once again I calculated all of these ahead of time, is 6%, so it's definitely
in the realm of possibility, but it's, the probability is much lower than having 99 out of 100 or
100 out of 100 being accurate, and then the probability, let
me put the double quotes here, the probability that it is accurate 96 out of 100 times is approximately 1.5%, and then the probability,
and I'll just do one more, I could keep going, the probability, you know, there's some probability that even though each
test has a 99% chance you just get super unlucky
and that, you know, very few of them are
accurate, well I'll just, and you see, you see what's happening to the probabilities as we have fewer and fewer of them being accurate, it becomes less and less probable. So the probability that 95 out of the 100 are accurate is, is approximately 0.3%. So this was just kind of a, I guess you could say
a thought experiment. If we had a test that we know for sure that every time you administer it, the probability that
it is accurate is 99%, then these are the probabilities that if you administered it 100 times, that you get 100 out 100 accurate, the probability that you
get 99 out of 100 accurate, and so on and so forth. So let's just keep that in mind, and then think a little bit
about hypothesis testing, and how we can use this framework. So let's put all that in
the back of our minds, and let's say that you
have devised a new test, you have a new test, and you
don't know how accurate it is. You have a new cholesterol test, you don't know how accurate it is, you know that in order
for it to be approved by whatever governing body
it has to be accurate 99, the probability of it being
accurate has to be 99%. So needs to have probability of accurate, accurate equal to 99%. You don't know if this is true, you just know that that's
what it needs to be. And so you have your test, and let's you set up a hypothesis, and your hypothesis
could be a lot of things, and once you get deeper into
statistics, there's, you know, null hypothesis and alternate hypotheses, but let's just start with
just a simple hypothesis, you're hopeful, your hypothesis
is that the probability that your new test is accurate is, this is your hypothesis,
because you want that to be your hypothesis cause
if you feel good about it, then you're like, okay,
yeah, maybe I'll get approved by the appropriate governing body. So you say, "Hey, my hypothesis is that "my new test is accurate
99, the probability "of it being accurate is 99%." So then you go off and
you apply it 100 times. So you apply your new test, you don't know the actual
probability of it being accurate, you apply the test 100 times. And let's say out of those 100 times you get that they are accurate, you get that it is
accurate, and you're able to use some other test that you, you know, some for-sure test, some
super accurate test, to verify your own test results, and you see that it is accurate
95 out of the 100 times. So the question you have is, well, does the hypothesis make sense to you? Will you accept this hypothesis? Well what you say is, well,
"If my hypothesis was true, "if my test were accurate 99, "if the probability of my
test being accurate is 99%, "what's the probability of
me getting this outcome?" Well, we figured that out. If it really was accurate 99% of the time, then the probability of getting
this outcome is only 0.3%. So if you assume true,
if you assume hypothesis, hypothesis, I'll just write "hyp," if you assume the hypothesis is true, the probability of the outcome you got, probability of observed
outcome is approximately 0.3%. And so you say, "Look, you know, "maybe, it's definitely possible "that I just got very,
very, very, very unlikely, "but based on this, I probably
should reject my hypothesis, "because the probability
of me getting this outcome, "if the hypothesis was true, "is very, very, very, very low." And as we go deeper into statistics, you'll see that there are
thresholds that people often set, for, you know, if the probability
of something happening or not happening is above
or below some threshold, then we might reject a certain hypothesis. But in this world, you
could see that, look, if my test really was
accurate 99% of the time, for me to get, when I
apply it to 100 people, it's only accurate 95 out of 100, if my hypothesis is true,
that would have only, there's only a 0.3% chance that I would have seen this observation. So based on that, it might be
completely reasonable to say, "You know what, I might
reject my hypothesis, "look for a new test, I don't feel good "about this new cholesterol
test that I constructed."