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## The idea of significance tests

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# Examples of null and alternative hypotheses

## Video transcript

- [Instructor] We are told
a restaurant owner installed a new automated drink machine. The machine is designed to
dispense 530 milliliters of liquid on the medium size setting. The owner suspects that the
machine may be dispensing too much in medium drinks. They decide to take a
sample of 30 medium drinks to see if the average amount is significantly greater
than 500 milliliters. What are appropriate hypotheses for their significance test? And they actually give
us four choices here. I'll scroll down a little
bit so that you can see all of the choices. So like always, pause
this video and see if you can have a go at it. Okay now let's do this together. So let's just remind ourselves
what a null hypothesis is and what an alternative hypothesis is. One way to view a null hypothesis, this is the hypothesis where things are happening as expected. Sometimes people will describe this as the no difference hypothesis. It'll often have a
statement of equality where the population parameter is equal to a value
where the value is what people were kind of assuming all along. The alternative hypothesis, this is a claim where if you have evidence to back up that claim, that would be new news. You are saying hey there's
something interesting going on here. There is a difference. And so in this context, the no difference, we would say the null hypothesis would be, we would care about the
population parameter, and here we care about the
average amount of drink dispensed in the medium setting. So the population parameter
there would be the mean, and that the mean would be
equal to 530 milliliters. Because that's what the drink
machine is supposed to do. And then the alternative hypothesis, this is what the owner fears, is that the mean actually
might be larger than that, larger than 530 milliliters. And so let's see which
of these choices is this? Well these first two choices
are talking about proportion, but it's really the average
amount that we're talking about. We see it up here. They decide to take a
sample of 30 medium drinks to see if the average amount, they're not talking
about proportions here, they're talking about averages, and in this case we're
talking about estimating the population parameter,
the population mean, for how much drink is
dispensed on that setting. And so this one is looking
like this right over here. Only these two are even
dealing with the mean. And the difference between
this one and this one is this says the mean is
greater than 530 milliliters, and that indeed is the owner's fear. And this over here, this
alternative hypothesis, is that the, that it's
dispensing on average less than 530 milliliters, but that's not what
the owner is afraid of. And so that's not the kind of the news that we're trying to
find some evidence for. So I would definitely pick choice C. Let's do another example. The National Sleep Foundation recommends that teenagers aged 14 to 17 years old get at least eight
hours of sleep per night for proper health and wellness. A statistics class at a
large high school suspects that students at their school are getting less than eight hours of sleep on average. To test their theory, they randomly sample 42 of these students and ask them how many hours
of sleep they get per night. The mean from this sample, the mean from the sample, is 7.5 hours. Here's their alternative hypothesis. The average amount of sleep
students at their school get per night is... What is an appropriate ending to their alternative hypothesis? So pause this video and see
if you can think about that. So let's just first think
about a good null hypothesis. So the null hypothesis is, hey there's actually no news here, that everything is what
people were always assuming. And so the null hypothesis
here is that no, the students are getting
at least eight hours of sleep per night. And so that would be, that remember we care about
the population of students. And so and we care about
the population of students at the school. And so we would say
well the null hypothesis is that the parameter for
the students at that school, the mean amount of sleep
that they're getting, is indeed greater than
or equal to eight hours. And a good clue for the
alternative hypothesis is when you see something
like this where they say, a statistics class at a
large high school suspects, so they suspect that
things might be different than what people have always been assuming or actually what's good for students. And so they suspect that
students at their school are getting less than eight
hours of sleep on average. And so they suspect that
the population parameter, the population mean, for
their school is actually less than eight hours. And so if you wanted to write this out in words, the average amount of sleep
students at their school get per night is less than eight hours. Now one thing to watch out for is one, you wanna make sure you're
getting the right parameter. Sometimes it's often a population mean. Sometimes it's a population proportion. But the other thing that sometimes folks get stuck up on, but the other thing that
sometimes confuses folks is, well we are measuring, is that we are calculating a statistic from a sample. Here we're calculating the sample mean, but that, the sample
statistics are not what should be involved in your hypotheses. Your hypotheses are claims
about your population that you care about, here the population is the
students at the high school.