Types of statistical studies

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Kind: captions
Language: en

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- [Instructor] Talk about the main types

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of statistical studies.

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So you can have a sample study

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and we've already talked
about this in several videos

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but we'll go over it again in this one.

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You can have an observational
study, observational study.

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Or you can have an experiment, experiment.

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So let's go through each of these

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and always pause this
video and see if you can

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think about what these words likely mean,

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or you might already know.

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Well, sample study, we have looked at.

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This is really where
you're trying to estimate

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the value of a parameter for a population.

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So what's an example of that?

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So let's say we take the
population of people in a city,

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and so that could be hundreds
of thousands of people,

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and the parameter that you care about is

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how much time on average do
they spend on a computer.

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So the parameter would be
for the entire population.

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If it was possible, you
would go talk to every,

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maybe there's a million
people in the city.

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You would talk to all
million of those people

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and ask them how much time
they spend on a computer

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and you would get the average

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and then that would be the parameter.

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So population parameter,
population parameter,

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would be average time
on a computer per day,

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average daily time, time on a computer.

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Now you'd determine that it's impractical

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to go talk to everyone, so
you're not going to be able

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to figure out the exact
population parameter,

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average daily time on a computer,

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so instead, you do a sample study.

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You randomly sample, and
there's a lot of thought

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in thinking about whether
your sample is truly random,

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so you randomly sample and there's also

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different techniques of randomly sampling.

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So you randomly sample
people from your population

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and then you take the average
daily time on a computer

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for your sample, and that
is going to be an estimate

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for the population parameter.

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So that's your classic sample study.

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Now in an observational study,

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you're not trying to estimate a parameter.

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You're trying to understand
how two parameters

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in a population might
move together or not.

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So let's say that you
have a population now,

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so let's say you have a population of,

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let's say you have a
population of 1,000 people.

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1,000 people, and you're
curious about whether

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average daily time on a computer,

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how it relates to people's blood pressure.

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So average computer time,

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oh, I shouldn't be writing this way.

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Instead of average computer time,

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it should just be computer time.

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Computer time versus blood
pressure, blood pressure.

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So what you do is you apply
a survey to all 1,000 people

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and you ask them how much
time you spend on a computer

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and what is your blood pressure?

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Or maybe you measure it in some way,

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and then you plot it
all, you look at the data

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and you see if those two
variables move together.

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So what does that mean?

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Well, let me draw.

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If this axis is, let's
say this is computer time.

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Computer time, and this
axis is blood pressure.

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Blood pressure.

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So let's say that there's one person

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who doesn't spend a lot
of time on a computer

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and they have a relatively
low blood pressure.

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There's another person
who spends a lot of time,

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has high blood pressure.

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There could be someone who
doesn't spend much time

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on a computer but has a
reasonably high blood pressure,

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but you keep doing this and
you get all these data points

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for those 1,000 people, and
I'm not going to sit here

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and draw 1,000 points, but
you see something like this,

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and so you see, hey, look, it looks like

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there's definitely some outliers

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but it looks like these two
variables move together.

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It looks like, in general,
the more computer time,

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the higher the blood pressure,

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or the higher the blood
pressure, the more computer time.

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And so you can make a conclusion here

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about these two variables correlating,

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that they're positively correlated.

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There is a positive, a
reasonable conclusion

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if you did the study
appropriately would be

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that more computer time correlates

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with higher blood pressure

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or that higher blood pressure correlates

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with more computer time.

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Now, when you do these
observational studies

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or when you interpret these
observational studies,

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when you read someone
else's, it's very important

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not to say oh, well, this shows me

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that computer time causes blood pressure,

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because this is not showing causality,

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and you also can't say,
maybe you might say,

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somehow blood pressure causes more people

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to spend time in front of a computer.

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That seems even a little bit sillier,

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but they're actually the same

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'cause all you're saying is
that there's a correlation.

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These two variables move together.

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You can't make a
conclusion about causality,

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that computer time causes blood pressure

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or that high blood pressure
causes more computer time.

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Why can't you make that?

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Well, there could be what's
called a confounding variable,

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sometimes called a lurking variable,

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where let's say that, so
this is computer time.

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Computer time, and this is blood pressure.

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Looks like building, so
blood, blood pressure.

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And it looks like these
two things move together.

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We saw that right over here in our data,

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but there could be a root variable

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that drives both of these,
a confounding variable,

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and that could just be the amount

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of physical activity someone has.

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So there could just be a
lack of physical activity

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driving both, lack of activity.

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People who are less active spend

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more time in front of a computer,

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and people who are less active
have higher blood pressure,

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and if you were to control for this,

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if you were to take a bunch of people

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who had a similar lack of activity

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or had a similar level of activity,

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you might see that computer time

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does not correlate with blood pressure,

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that these are just both
driven by the same thing

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and what you're really
seeing here is like, okay,

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people who aren't active
drives both of these variables.

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So once again, when you do
this observational study

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and if you do it well,
you can draw correlations

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and that might give you decent
hypotheses for causality,

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but this does not show causality

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because you could have
these confounding variables.

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Now, experiments, and experiments are

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the basis of the scientific method.

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Experiments are all about
trying to establish causality,

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and so what you would do is

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if you wanted to do an
experiment, you would take,

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and you probably wouldn't be able

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to do it with 1,000 people.

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Experiments in some ways are

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the hardest to do of all of these.

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Maybe you take 100 people, 100 people,

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and to avoid having this
confounding variable

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introduce error into your experiment,

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you randomly assign these
hundred people to two groups.

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So random assign, it's very important

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that they're randomly assigned.

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And that's nice, you might not know

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all of the confounding variables there,

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but it makes it likely
that each group will have

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a same amount of people
with lack of activity

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or the activity levels on
average in each of the groups,

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when they're randomly assigned,
it gives you a better chance

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that one group doesn't have
a significantly different

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activity level than the other.

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And then what you do is you have

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a control group and you
have a treatment group.

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Once again, you've randomly assigned them.

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So a control and then treatment.

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And what you might say is, okay,

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for some amount of time, all
of you in the control group

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can only spend max of 30
minutes in front of a computer,

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or maybe if you really
wanted to do it, you'd say

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you have to spend exactly
30 minutes on a computer

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and that's maybe a little unrealistic,

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and then the treatment
group, you have to say,

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you have to spend exactly two
hours in front of a computer,

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and I'm making up these numbers at random,

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and it would be nice to see, okay,

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what as everyone's blood
pressure before the experiment?

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And you'd say, okay, well,
the averages are similar

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going into the experiment,

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and then you go some amount of time

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and you measure blood pressure,

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and if you see that, wow, this group

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definitely has a higher blood pressure,

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this group has a higher blood pressure,

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so the blood pressure is higher here,

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and once again, some of this might have

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just happened randomly, it
might've been the people

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you happened to put in
there, et cetera, et cetera,

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but depending if this was
a large enough experiment

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and you conducted it well,
this says, hey, look,

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I'm feeling like there
is a causality here,

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that by making these
people spend more time

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in front of a computer,

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that that actually raised
their blood pressure.

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So once again, sample study,

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you're trying to estimate
a population parameter.

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Observation study, you are
seeing if there is a correlation

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between two things and
you have to be careful

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not to say, hey, one is causing the other

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'cause you could have
confounding variables.

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Experiment, you're trying to establish

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or show causality and you do
that by taking your group,

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randomly assigning to
a control or treatment.

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That should evenly or
hopefully evenly distribute.

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Not always, there's
some chance it doesn't,

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but distribute the confounding variables

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and then on each group,
you change how much

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of one of these variables they get

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and you see if it drives
the other variable.

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So anyway, in the next two videos,

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we'll do some examples of identifying

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these types of sample studies

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and thinking about what
we can conclude from them,

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or these types of statistical studies

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and see what we can conclude from them.


Techniques for random sampling and avoiding bias

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Language: en

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- [Instructor] Let's
say that we run a school

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and in that school there is a population

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of students right over here.

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And that is our population.

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And we want to get a sense of how

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these students feel about the
quality of math instruction

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at the school, so we construct a survey,

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and we just need to decide
who are we going to get

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to actually answer this survey.

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One option is to just go to
every member of the population,

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but let's just say it's
a really large school.

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Let's say we're a college

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and there's 10,000 people in the college.

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We say, well, we can't
just talk to everyone.

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So instead, we say, let's
sample this population

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to get an indication of how
the entire school feels.

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So we are going to sample it.

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We are going to sample that population.

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Now in order to avoid having bias

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in our response, in order for it to

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have the best chance of
it being indicative of the

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entire population, we want
our sample to be random.

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So our sample could either be random,

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random, or not random.

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Not random.

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And it might seem, at first,
pretty straightforward

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to do a random sample, but when
you actually get down to it,

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it's not always as straightforward
as you would think.

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So one type of random sample
is just a simple random sample.

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So, simple, simple,

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random, random, sample,

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and this is saying, alright, let me

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maybe assign a number to
every person in the school,

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maybe they already have
a student ID number,

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and I'm just going to get a computer,

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a random number generator, to generate the

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100 people, the 100 students,

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so let's say there's a
sample of 100 students,

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that I'm going to apply the survey to,

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so that would be a simple random sample.

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We are just going into this
whole population and randomly,

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let me just draw this.

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So this is the population,
we are just randomly

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picking people out, and we
know it's random because

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a random number generator, or
we have a string of numbers

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or something like that,
that is allowing us

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to pick the students.

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Now that's pretty good, it's
unlikely that you're going

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to have bias from this
sample, but there is some

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probability that, just by chance,

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your random number generator
just happened to select

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maybe a disproportionate
number of boys over girls,

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or a disproportionate number of freshmen,

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or a disproportionate
number of engineering majors

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versus English majors,
and that's a possibility.

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So even though you are
taking a simple random sample

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that is truly random, once
again, it's some probability

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that it's not indicative
of the entire population.

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And so to mitigate that,
there are other techniques

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at our disposal.

00:02:50.385 --> 00:02:53.468
One technique is a stratified sample.

00:02:54.506 --> 00:02:55.423
Stratified.

00:02:56.735 --> 00:02:59.940
And so this is the idea of
taking our entire population

00:02:59.940 --> 00:03:01.356
and essentially stratifying it.

00:03:01.356 --> 00:03:04.712
So let's say we want to, we
take that same population,

00:03:04.712 --> 00:03:06.952
we take that same
population, I'll draw it as a

00:03:06.952 --> 00:03:09.135
square here just for convenience,

00:03:09.135 --> 00:03:11.329
and we're gonna stratify it by,

00:03:11.329 --> 00:03:13.735
let's say we're concerned that
we get a appropriate sample

00:03:13.735 --> 00:03:16.760
of freshmen, sophomores,
juniors, and seniors.

00:03:16.760 --> 00:03:20.846
So we'll stratify it by
freshmen, sophomores,

00:03:20.846 --> 00:03:24.887
juniors, and seniors, and then we sample

00:03:24.887 --> 00:03:27.650
25 from each of these groups.

00:03:27.650 --> 00:03:29.089
So these are the stratifications.

00:03:29.089 --> 00:03:33.256
This is freshmen, sophomore,
juniors, and seniors,

00:03:34.314 --> 00:03:37.515
and instead of just sampling
100 out of the entire pool,

00:03:37.515 --> 00:03:40.182
we sample 25 from each of these.

00:03:42.217 --> 00:03:44.110
So just like that.

00:03:44.110 --> 00:03:46.687
And so that makes sure that you are

00:03:46.687 --> 00:03:49.421
getting indicative responses from

00:03:49.421 --> 00:03:52.754
at least all of the different age groups

00:03:54.020 --> 00:03:56.488
or levels within your university.

00:03:56.488 --> 00:03:57.789
Now there might be another
issue where you say,

00:03:57.789 --> 00:04:00.784
well, I'm actually more
concerned that we have

00:04:00.784 --> 00:04:04.894
accurate representation of
males and females in the school,

00:04:04.894 --> 00:04:06.525
and there is some probability,

00:04:06.525 --> 00:04:08.510
you know, if I do 100
random people, it's very

00:04:08.510 --> 00:04:11.471
likely that it's close to
50/50, but there's some chance,

00:04:11.471 --> 00:04:14.373
just due to randomness,
there's disproportionately male

00:04:14.373 --> 00:04:16.022
or disproportionately female.

00:04:16.022 --> 00:04:18.994
And that's even possible
in the stratified case.

00:04:18.994 --> 00:04:20.224
And so what you might say is,

00:04:20.224 --> 00:04:21.827
well, you know what I'm gonna do?

00:04:21.827 --> 00:04:25.678
I'm going to, there's a technique
called a clustered sample.

00:04:25.678 --> 00:04:29.184
Let me write this right
over here, clustered,

00:04:29.184 --> 00:04:33.526
a clustered sample, and what
we do is we sample groups.

00:04:33.526 --> 00:04:36.348
Each of those groups we feel confident has

00:04:36.348 --> 00:04:38.112
a good balance of male females.

00:04:38.112 --> 00:04:40.735
So, for example, we might,

00:04:40.735 --> 00:04:44.205
instead of sampling individuals
from the entire population,

00:04:44.205 --> 00:04:46.852
we might say, look, you know,

00:04:46.852 --> 00:04:49.754
on Tuesdays and Thursdays, and this, well,

00:04:49.754 --> 00:04:51.237
even there as you can tell this is not a

00:04:51.237 --> 00:04:55.569
trivial thing to do, let's
just say that we can split,

00:04:55.569 --> 00:04:58.652
let's say we can split our population

00:04:59.582 --> 00:05:02.449
into groups, maybe these are classrooms,

00:05:02.449 --> 00:05:05.851
and each of these classrooms
have an even distribution

00:05:05.851 --> 00:05:10.681
of males and females, or pretty
close to even distributions.

00:05:10.681 --> 00:05:13.815
And so what we do is we
sample the actual classrooms,

00:05:13.815 --> 00:05:16.428
so that's why it's called
cluster, or cluster technique,

00:05:16.428 --> 00:05:19.850
or clustered random
sample, because we're going

00:05:19.850 --> 00:05:24.029
to randomly sample our
classrooms, each of which have a

00:05:24.029 --> 00:05:27.338
close or maybe a exact
balance of males and females

00:05:27.338 --> 00:05:29.056
so we know that we're gonna
get good representation,

00:05:29.056 --> 00:05:31.843
but we are still sampling,
we are sampling from

00:05:31.843 --> 00:05:33.491
the clusters, but then we're gonna survey

00:05:33.491 --> 00:05:35.709
every single person in
each of these clusters,

00:05:35.709 --> 00:05:38.890
every single person in
one of these classrooms.

00:05:38.890 --> 00:05:43.882
So, once again, these are
all forms of random surveys,

00:05:43.882 --> 00:05:45.995
or random samples, you have
the simple random sample,

00:05:45.995 --> 00:05:48.840
you can stratify, or
you can cluster and then

00:05:48.840 --> 00:05:51.452
randomly pick the clusters and then survey

00:05:51.452 --> 00:05:54.169
everyone in that cluster.

00:05:54.169 --> 00:05:57.315
Now if these are all random samples,

00:05:57.315 --> 00:05:59.881
what are the non-random things like?

00:05:59.881 --> 00:06:02.191
Well, one case of
non-random, you could have a

00:06:02.191 --> 00:06:03.608
voluntary survey,

00:06:06.719 --> 00:06:08.632
or voluntary sample,
and this might just be

00:06:08.632 --> 00:06:10.176
you tell every student at the school,

00:06:10.176 --> 00:06:12.707
"Hey, here's a web address.

00:06:12.707 --> 00:06:15.400
"If you're interested, come
and fill out this survey."

00:06:15.400 --> 00:06:18.163
And that's likely to
introduce bias because

00:06:18.163 --> 00:06:20.485
you might have maybe the
students who really like

00:06:20.485 --> 00:06:22.680
the math instruction at their school

00:06:22.680 --> 00:06:24.995
more likely to fill it out,
maybe the students who really

00:06:24.995 --> 00:06:27.062
don't like it are more
likely to fill it out,

00:06:27.062 --> 00:06:29.036
maybe it's just the
kids who have more time

00:06:29.036 --> 00:06:30.266
more likely to fill it out.

00:06:30.266 --> 00:06:34.365
So this has a good chance
of introducing bias.

00:06:34.365 --> 00:06:36.853
The students who fill out the survey

00:06:36.853 --> 00:06:40.701
might be just more skewed
one way or the other because,

00:06:40.701 --> 00:06:43.534
you know, they volunteered for it.

00:06:45.060 --> 00:06:48.477
Another not random sample would be called

00:06:51.596 --> 00:06:54.394
you're introducing bias
because of convenience

00:06:54.394 --> 00:06:56.031
is the term that's often used,

00:06:56.031 --> 00:06:58.028
and this might say, well,
let's just sample the 100

00:06:58.028 --> 00:07:00.002
first students who show up in school.

00:07:00.002 --> 00:07:01.976
And that's just convenient for me because

00:07:01.976 --> 00:07:03.438
I didn't have to use random numbers,

00:07:03.438 --> 00:07:06.512
or do the stratification, or
doing any of this clustering,

00:07:06.512 --> 00:07:09.600
but you can understand how
this also would introduce bias,

00:07:09.600 --> 00:07:11.853
because the first 100 students
who show up at school,

00:07:11.853 --> 00:07:14.105
maybe those are the
most diligent students,

00:07:14.105 --> 00:07:17.507
maybe they all take an
early math class that has

00:07:17.507 --> 00:07:20.011
a very good instructor where
they're all happy about it.

00:07:20.011 --> 00:07:21.067
Or it might go the other way,

00:07:21.067 --> 00:07:22.968
the instructor there
isn't the best one, and so

00:07:22.968 --> 00:07:25.499
it might introduce bias the other way.

00:07:25.499 --> 00:07:27.751
So if you let people
volunteer or you just say,

00:07:27.751 --> 00:07:29.597
"Oh, let me do the first N students."

00:07:29.597 --> 00:07:32.087
Or you say, "Hey, let me just
talk to all of the students

00:07:32.087 --> 00:07:35.477
"who happen to be in
front of me right now."

00:07:35.477 --> 00:07:37.068
They might be in front of
you out of convenience,

00:07:37.068 --> 00:07:40.651
but they might not be
a true random sample.

00:07:41.921 --> 00:07:44.661
Now there is other reasons
why you might introduce bias,

00:07:44.661 --> 00:07:47.413
and it might not be
because of the sampling.

00:07:47.413 --> 00:07:49.038
You might introduce bias because of the

00:07:49.038 --> 00:07:51.104
wording of your survey.

00:07:51.104 --> 00:07:53.438
You could imagine a survey that says,

00:07:53.438 --> 00:07:56.515
do you consider yourself
lucky to get a math education

00:07:56.515 --> 00:07:59.580
that very few other people
in the world have access to?

00:07:59.580 --> 00:08:01.263
Well, that might bias you to say,

00:08:01.263 --> 00:08:03.249
"Well, yeah, I guess I feel lucky."

00:08:03.249 --> 00:08:05.790
Well, if the wording was,

00:08:05.790 --> 00:08:08.269
do you like the fact
that a disproportionate

00:08:08.269 --> 00:08:11.950
more students at your school tend to fail

00:08:11.950 --> 00:08:14.713
algebra than our surrounding schools?

00:08:14.713 --> 00:08:16.617
Well, that might bias you negatively.

00:08:16.617 --> 00:08:19.578
So the wording really, really,
really matters in surveys,

00:08:19.578 --> 00:08:22.956
and there is a lot that
would go into this.

00:08:22.956 --> 00:08:25.545
And the other one is just people's,

00:08:25.545 --> 00:08:27.205
you know, it's called response bias.

00:08:27.205 --> 00:08:29.481
And, once again, this isn't about...

00:08:29.481 --> 00:08:30.648
Response bias.

00:08:32.455 --> 00:08:35.509
And this is just people not
wanting to tell the truth

00:08:35.509 --> 00:08:37.471
or maybe not wanting to respond at all.

00:08:37.471 --> 00:08:39.178
Maybe they're afraid that somehow

00:08:39.178 --> 00:08:40.839
their response is gonna show up

00:08:40.839 --> 00:08:42.704
in front of their math
teacher or the administrators,

00:08:42.704 --> 00:08:44.132
or if they're too negative,

00:08:44.132 --> 00:08:46.106
it might be taken out on them in some way.

00:08:46.106 --> 00:08:48.474
And because of that, they
might not be truthful,

00:08:48.474 --> 00:08:51.086
and so they might be overly positive

00:08:51.086 --> 00:08:53.153
or not fill it out at all.

00:08:53.153 --> 00:08:56.946
So anyway, this is a
very high level overview

00:08:56.946 --> 00:08:58.653
of how you could think about sampling.

00:08:58.653 --> 00:09:00.522
You want to go random
because it lowers the

00:09:00.522 --> 00:09:03.840
probability of their
introducing some bias into it.

00:09:03.840 --> 00:09:05.723
And then these are some techniques.

00:09:05.723 --> 00:09:07.104
And also think about
whether you're falling

00:09:07.104 --> 00:09:09.403
into some of these pitfalls
that have a good chance

00:09:09.403 --> 00:09:11.070
of introducing bias.


Worked example identifying observational study

WEBVTT
Kind: captions
Language: en

00:00:00.241 --> 00:00:01.482
- [Instructor] So we have a type of

00:00:01.482 --> 00:00:03.907
statistical study described here.

00:00:03.907 --> 00:00:05.675
I encourage you to pause
this video, read it,

00:00:05.675 --> 00:00:06.658
and see if you can figure out,

00:00:06.658 --> 00:00:09.978
is this a sample study, is
it an observational study,

00:00:09.978 --> 00:00:11.270
is it an experiment?

00:00:11.270 --> 00:00:13.592
And then also think about
what type of conclusions

00:00:13.592 --> 00:00:17.747
can you make based on the
information in this study.

00:00:17.747 --> 00:00:20.122
Alright, now let's work on this together.

00:00:20.122 --> 00:00:22.987
British researchers were
interested in the relationship

00:00:22.987 --> 00:00:25.830
between farmers' approach to their cows

00:00:25.830 --> 00:00:28.211
and cows' milk yield.

00:00:28.211 --> 00:00:30.521
They prepared a survey
questionnaire regarding

00:00:30.521 --> 00:00:33.840
the farmers' perception of
the cows' mental capacity,

00:00:33.840 --> 00:00:35.966
the treatment they give to the cows,

00:00:35.966 --> 00:00:37.524
and the cows' yield.

00:00:37.524 --> 00:00:41.839
The survey was filled by all
the farms in Great Britain.

00:00:41.839 --> 00:00:45.067
After analyzing their results,
they found that on farms

00:00:45.067 --> 00:00:48.710
where cows were called by name, milk yield

00:00:48.710 --> 00:00:51.746
was 258 liters higher on average

00:00:51.746 --> 00:00:54.277
than on farms where this was not the case.

00:00:54.277 --> 00:00:55.408
Alright, so they're making a connection

00:00:55.408 --> 00:00:56.960
between two variables.

00:00:56.960 --> 00:01:00.566
One was whether cows called by name,

00:01:00.566 --> 00:01:01.399
whether,

00:01:02.332 --> 00:01:04.494
whether cows named, alright.

00:01:04.494 --> 00:01:05.850
Whether

00:01:05.850 --> 00:01:07.265
cows

00:01:07.265 --> 00:01:08.849
named

00:01:08.849 --> 00:01:10.485
and this would be a categorical variable

00:01:10.485 --> 00:01:12.446
because for any given farmer

00:01:12.446 --> 00:01:15.473
it's gonna be a yes or a
no, that the cows are named,

00:01:15.473 --> 00:01:16.886
and so they're trying to form a connection

00:01:16.886 --> 00:01:19.979
between whether the cows are named, and,

00:01:19.979 --> 00:01:21.229
and milk yield.

00:01:22.128 --> 00:01:23.528
And this would be a quantitative variable,

00:01:23.528 --> 00:01:24.909
'cause you can, you're measuring it

00:01:24.909 --> 00:01:26.866
in terms of number of liters.

00:01:26.866 --> 00:01:28.366
Milk, milk, yield.

00:01:29.207 --> 00:01:32.841
Whether we are drawing a connection.

00:01:32.841 --> 00:01:35.429
And they are able to draw
some form a connection.

00:01:35.429 --> 00:01:38.287
They're saying, hey when the
cows were called by name,

00:01:38.287 --> 00:01:41.870
milk yield was 258
liters higher on average

00:01:43.042 --> 00:01:45.675
than on farms where this was not the case.

00:01:45.675 --> 00:01:48.239
So first is the thing of what
type of statistical study

00:01:48.239 --> 00:01:50.217
this is, and we could think, okay,

00:01:50.217 --> 00:01:52.803
is this a sample study,

00:01:52.803 --> 00:01:54.195
is this a sample study,

00:01:54.195 --> 00:01:56.859
is this an observational study,

00:01:56.859 --> 00:01:58.830
observational,

00:01:58.830 --> 00:02:00.913
or is this an experiment?

00:02:01.774 --> 00:02:03.662
Now, a sample study,

00:02:03.662 --> 00:02:04.579
experiment,

00:02:06.070 --> 00:02:09.303
a sample study, you would
be trying to estimate

00:02:09.303 --> 00:02:12.279
a parameter for a broader population.

00:02:12.279 --> 00:02:14.241
Here, it's not so much
that they're estimating

00:02:14.241 --> 00:02:16.156
the parameter, they're
trying to see the connection

00:02:16.156 --> 00:02:19.173
between two variables, and that

00:02:19.173 --> 00:02:21.106
brings us to observational study,

00:02:21.106 --> 00:02:23.349
'cause that's what an
observational study is all about.

00:02:23.349 --> 00:02:26.116
Can we draw a connection, can we draw

00:02:26.116 --> 00:02:28.320
a positive or a negative correlation

00:02:28.320 --> 00:02:31.693
between variables based on observations?

00:02:31.693 --> 00:02:33.505
So we surveyed a population here,

00:02:33.505 --> 00:02:35.646
the farmers in Great Britain,

00:02:35.646 --> 00:02:38.427
and we are able to draw some type of

00:02:38.427 --> 00:02:40.616
connection between these variables.

00:02:40.616 --> 00:02:43.626
And so this is clearly an observational,

00:02:43.626 --> 00:02:45.409
an observational study.

00:02:45.409 --> 00:02:47.229
Now this is not an experiment.

00:02:47.229 --> 00:02:50.057
If it was an experiment,
we would take the farmers

00:02:50.057 --> 00:02:53.552
and we would randomly assign
them into one or two groups.

00:02:53.552 --> 00:02:57.920
And in one group we would
say, don't name, no name,

00:02:57.920 --> 00:03:00.148
no naming, and in the
other group we would say,

00:03:00.148 --> 00:03:01.967
name your cows.

00:03:01.967 --> 00:03:03.591
And then we would wait some period of time

00:03:03.591 --> 00:03:05.109
and we would see the
average milk production

00:03:05.109 --> 00:03:07.198
going into the experiment
in the no naming group

00:03:07.198 --> 00:03:09.572
and the naming group,
and then we would see,

00:03:09.572 --> 00:03:12.080
we wait some period of
time, six months, a year,

00:03:12.080 --> 00:03:14.420
and then we would see the
average milk production

00:03:14.420 --> 00:03:17.750
after either not naming or
naming the cows for six months.

00:03:17.750 --> 00:03:18.990
So this is not what occurred here.

00:03:18.990 --> 00:03:20.810
Here we just did the survey to everybody

00:03:20.810 --> 00:03:22.406
and we just asked them this question,

00:03:22.406 --> 00:03:24.079
and we were able to find this,

00:03:24.079 --> 00:03:25.612
we were able to find this connection

00:03:25.612 --> 00:03:27.219
between whether the cows were named

00:03:27.219 --> 00:03:28.432
and the actual milk yield.

00:03:28.432 --> 00:03:32.166
So clearly, not an experiment,
this was an observational,

00:03:32.166 --> 00:03:33.889
observational study.

00:03:33.889 --> 00:03:37.269
Now the next thing is,
what can we conclude here?

00:03:37.269 --> 00:03:38.962
We know when, you know, they're telling us

00:03:38.962 --> 00:03:39.998
that when the cows were named,

00:03:39.998 --> 00:03:43.230
it looks like there was a 258 liter

00:03:43.230 --> 00:03:45.983
higher yield on average.

00:03:45.983 --> 00:03:48.756
So, the conclusion that
we can strictly make here,

00:03:48.756 --> 00:03:50.999
is like, well, for

00:03:50.999 --> 00:03:55.480
for farmers in Great Britain
there's a correlation,

00:03:55.480 --> 00:03:58.920
a positive correlation
between whether cows are named

00:03:58.920 --> 00:04:00.735
and the milk yield.

00:04:00.735 --> 00:04:02.165
So that we can say for sure.

00:04:02.165 --> 00:04:03.715
So let me write that down.

00:04:03.715 --> 00:04:05.342
So for Great Britain,

00:04:05.342 --> 00:04:07.925
for, for Great Britain farmers,

00:04:09.144 --> 00:04:10.977
Great Britain farmers,

00:04:12.675 --> 00:04:13.508
farmers,

00:04:14.417 --> 00:04:17.765
we have a positive correlation.

00:04:17.765 --> 00:04:19.432
Positive correlation

00:04:21.149 --> 00:04:22.816
between naming cows,

00:04:24.334 --> 00:04:25.501
between naming

00:04:28.993 --> 00:04:29.826
cows

00:04:30.914 --> 00:04:32.983
and milk yield.

00:04:32.983 --> 00:04:34.233
And milk yield.

00:04:35.198 --> 00:04:37.596
That's pretty much what we can say here.

00:04:37.596 --> 00:04:40.561
Now, some people might be
tempted to try to draw causality.

00:04:40.561 --> 00:04:43.025
We'll see this all the
time where you see these

00:04:43.025 --> 00:04:44.584
observational studies,
and people try to hint

00:04:44.584 --> 00:04:46.387
that maybe there's a
causal relationship here,

00:04:46.387 --> 00:04:49.113
maybe the naming is actually what makes

00:04:49.113 --> 00:04:51.682
the milk yield go up.

00:04:51.682 --> 00:04:52.916
Or maybe it's the other way,

00:04:52.916 --> 00:04:54.473
the cows produce a lot of milk,

00:04:54.473 --> 00:04:56.788
the farmers like them more
and they wanna name them,

00:04:56.788 --> 00:04:59.899
because it's like hey, that's
my high milk producing cow.

00:04:59.899 --> 00:05:01.612
So, there's a lot of,

00:05:01.612 --> 00:05:04.022
there's a lot of temptation to say,

00:05:04.022 --> 00:05:05.355
you know naming,

00:05:06.241 --> 00:05:10.924
that maybe there's a causality
that naming causes more milk,

00:05:10.924 --> 00:05:11.757
more milk,

00:05:12.868 --> 00:05:15.949
or that maybe more milk causes naming.

00:05:15.949 --> 00:05:17.870
You know, the farmers
really like that cow,

00:05:17.870 --> 00:05:19.018
so they start naming them,

00:05:19.018 --> 00:05:21.599
or whatever, whatever it might be.

00:05:21.599 --> 00:05:24.663
But you can't make this
causal relationship

00:05:24.663 --> 00:05:26.315
based on this observational study.

00:05:26.315 --> 00:05:27.381
You might've been able to do it

00:05:27.381 --> 00:05:29.687
with a well constructed experiment,

00:05:29.687 --> 00:05:31.480
but not with an observational study.

00:05:31.480 --> 00:05:33.441
And that's because there could be some

00:05:33.441 --> 00:05:36.938
confounding variable that
is driving both of them.

00:05:36.938 --> 00:05:39.568
So, for example, that confounding variable

00:05:39.568 --> 00:05:41.901
might just be a nice farmer.

00:05:43.624 --> 00:05:44.791
A nice farmer.

00:05:46.396 --> 00:05:48.323
And you know we can define
nice in a lot of ways,

00:05:48.323 --> 00:05:50.043
they're gentle, they,

00:05:50.043 --> 00:05:52.720
and a nice farmer is more likely to name,

00:05:52.720 --> 00:05:55.805
and a nice farmer's more likely to get,

00:05:55.805 --> 00:05:57.977
gets a higher yield.

00:05:57.977 --> 00:05:59.587
And the reason why this
is a confounding variable,

00:05:59.587 --> 00:06:01.040
if you were to control for that,

00:06:01.040 --> 00:06:04.636
if you just take, well let's
just control for nice farmers

00:06:04.636 --> 00:06:06.116
and then see if naming makes a difference.

00:06:06.116 --> 00:06:07.315
It might not make a difference.

00:06:07.315 --> 00:06:10.227
If the farmer is, you
know, petting the cows

00:06:10.227 --> 00:06:12.504
and treating them humanely,
and doing other things,

00:06:12.504 --> 00:06:14.089
it might not matter whether the,

00:06:14.089 --> 00:06:16.565
whether the farmer names them or not.

00:06:16.565 --> 00:06:19.468
Likewise, if you take
some less nice farmers

00:06:19.468 --> 00:06:21.940
who, you know, hit their cows, and

00:06:21.940 --> 00:06:23.927
and they have really inhumane conditions,

00:06:23.927 --> 00:06:24.975
it might not make a difference

00:06:24.975 --> 00:06:27.563
whether they name the cows or not.

00:06:27.563 --> 00:06:29.118
And so it's very important that you,

00:06:29.118 --> 00:06:30.969
from the observational studies, you might,

00:06:30.969 --> 00:06:33.344
if they're well constructed,
you might be able to make a,

00:06:33.344 --> 00:06:35.904
you might be able to say
there's a correlation,

00:06:35.904 --> 00:06:38.913
but, you won't be able to make a,

00:06:38.913 --> 00:06:42.580
drive a causal, or make
a causal conclusion.


Worked example identifying experiment

WEBVTT
Kind: captions
Language: en

00:00:00.000 --> 00:00:01.971
- [Instructor] We have some
type of study described here.

00:00:01.971 --> 00:00:03.794
I encourage you to pause this video,

00:00:03.794 --> 00:00:07.242
and think about whether this
is an observational study,

00:00:07.242 --> 00:00:10.667
it's an experiment, or
it's a sample study.

00:00:10.667 --> 00:00:14.243
And also think about whether
it was conducted well.

00:00:14.243 --> 00:00:16.472
All right, now let's do this together.

00:00:16.472 --> 00:00:18.364
A group of doctors was
interested in comparing

00:00:18.364 --> 00:00:21.569
the effectiveness of
placebo pills and real pills

00:00:21.569 --> 00:00:23.136
in treating migraines.

00:00:23.136 --> 00:00:26.073
Placebo pills are pills that look

00:00:26.073 --> 00:00:27.304
just like the regular pill,

00:00:27.304 --> 00:00:28.535
and from a patient's point of view,

00:00:28.535 --> 00:00:29.986
they don't know which on they're getting.

00:00:29.986 --> 00:00:32.041
The reason why we do this in studies

00:00:32.041 --> 00:00:36.244
is because there's something
called a placebo effect.

00:00:36.244 --> 00:00:38.577
Often times, just by taking something

00:00:38.577 --> 00:00:39.808
that you think is good for you,

00:00:39.808 --> 00:00:41.700
some medicine that you
think might help you,

00:00:41.700 --> 00:00:44.205
it actually does help you.

00:00:44.205 --> 00:00:46.594
We can think about why the
placebo effect happens,

00:00:46.594 --> 00:00:48.289
but this is well documented.

00:00:48.289 --> 00:00:50.878
So when people are
trying to test medicine,

00:00:50.878 --> 00:00:52.782
they wanna say, well,
does this have an effect

00:00:52.782 --> 00:00:55.621
above and beyond the placebo effect.

00:00:55.621 --> 00:00:59.795
That's why they are
putting them in these two,

00:00:59.795 --> 00:01:00.628
that's why they're comparing

00:01:00.628 --> 00:01:03.701
the real pills to the placebo pills.

00:01:03.701 --> 00:01:05.840
They randomly assigned
a group of 300 patients

00:01:05.840 --> 00:01:08.185
suffering from migraines into two groups.

00:01:08.185 --> 00:01:10.496
So they have their 300,

00:01:10.496 --> 00:01:14.362
they have their 300
patients right over here.

00:01:14.362 --> 00:01:18.065
And they're randomly putting
them into the placebo group.

00:01:18.065 --> 00:01:21.232
Placebo group, or the real pill group.

00:01:24.648 --> 00:01:26.181
One group was given a real pill,

00:01:26.181 --> 00:01:29.298
oh no, we already read that.

00:01:29.298 --> 00:01:32.931
Both groups were told which
kind of pill they got.

00:01:32.931 --> 00:01:37.134
That is sketchy because the
whole point about a placebo

00:01:37.134 --> 00:01:40.164
is that you think you got the real thing,

00:01:40.164 --> 00:01:41.964
or you think you might
have gotten the real thing.

00:01:41.964 --> 00:01:43.163
If you're told you got a placebo,

00:01:43.163 --> 00:01:45.308
that tends to undermine
the placebo effect.

00:01:45.308 --> 00:01:47.281
If you're just told
you're given a sugar pill,

00:01:47.281 --> 00:01:50.045
well, then the impact, well anyway.

00:01:50.045 --> 00:01:54.212
This is, that right over there
is definitely bad practice.

00:01:55.234 --> 00:01:57.614
So let's keep going over here.

00:01:57.614 --> 00:02:00.052
Before taking the pills,
and a day afterwards,

00:02:00.052 --> 00:02:01.910
the patients were asked
to fill out questionnaires

00:02:01.910 --> 00:02:03.292
regarding their condition.

00:02:03.292 --> 00:02:05.230
Then, the doctors analyzed
the overall changes

00:02:05.230 --> 00:02:07.912
in questionnaires for each
group and compared them.

00:02:07.912 --> 00:02:08.771
All right, so first of all,

00:02:08.771 --> 00:02:10.609
what type of study is this?

00:02:10.609 --> 00:02:12.022
Well, we're taking our groups,

00:02:12.022 --> 00:02:15.285
we are randomly putting them
into two different groups.

00:02:15.285 --> 00:02:18.617
You can call the placebo
group maybe the control group.

00:02:18.617 --> 00:02:20.068
And then you're putting the real pill

00:02:20.068 --> 00:02:22.088
as you're actual experimental group.

00:02:22.088 --> 00:02:24.577
So this is a classic experiment.

00:02:24.577 --> 00:02:26.474
This is a classic experiment.

00:02:26.474 --> 00:02:29.933
You're trying to establish
a causal relationship.

00:02:29.933 --> 00:02:31.988
You wanna see whether this real pill

00:02:31.988 --> 00:02:34.655
actually makes migraines better.

00:02:36.513 --> 00:02:37.930
Migraines better.

00:02:39.053 --> 00:02:40.365
So does it actually do it,

00:02:40.365 --> 00:02:43.035
and does it actually do
it better than a placebo?

00:02:43.035 --> 00:02:45.136
And you're randomly putting
the people in both groups

00:02:45.136 --> 00:02:48.468
to try to distribute any
confounding variables

00:02:48.468 --> 00:02:50.605
that there might be there.

00:02:50.605 --> 00:02:52.381
This is clearly an experiment.

00:02:52.381 --> 00:02:53.605
Now the other options,
you might have said,

00:02:53.605 --> 00:02:55.735
well, this may be an observational study?

00:02:55.735 --> 00:02:59.764
Remember, an observational
study, observational,

00:02:59.764 --> 00:03:01.865
that's more of where you
look at a population,

00:03:01.865 --> 00:03:02.698
you look at a group,

00:03:02.698 --> 00:03:04.083
you ask them a bunch of questions often,

00:03:04.083 --> 00:03:08.250
and you see if they're
correlations between two variables.

00:03:09.296 --> 00:03:12.129
So variable one, and variable two,

00:03:13.290 --> 00:03:14.486
and you're able to make

00:03:14.486 --> 00:03:17.919
some type of correlational statement.

00:03:17.919 --> 00:03:20.836
You're not trying to get at causality.

00:03:20.836 --> 00:03:23.506
In a sample study, a sample study,

00:03:23.506 --> 00:03:27.245
this is just you trying
to estimate a parameter

00:03:27.245 --> 00:03:28.975
for the entire population.

00:03:28.975 --> 00:03:31.642
So a sample study might of been,

00:03:32.698 --> 00:03:37.067
of the entire population what
percentage gets migraines.

00:03:37.067 --> 00:03:39.029
You can't talk to the entire population,

00:03:39.029 --> 00:03:40.771
maybe the entire population
is millions of people,

00:03:40.771 --> 00:03:43.313
so you take a sample of say 100 people.

00:03:43.313 --> 00:03:45.287
And you ask them, do you get migraines?

00:03:45.287 --> 00:03:47.713
Then you say, okay, that
percentage of our sample

00:03:47.713 --> 00:03:49.490
that get migraines, that's a good estimate

00:03:49.490 --> 00:03:50.395
for the parameter.

00:03:50.395 --> 00:03:52.853
What percentage of my entire population

00:03:52.853 --> 00:03:54.343
actually gets migraines?

00:03:54.343 --> 00:03:56.490
So this was clearly an experiment.

00:03:56.490 --> 00:03:57.559
Now the next question is, well,

00:03:57.559 --> 00:03:59.544
was this experiment conducted well?

00:03:59.544 --> 00:04:01.889
Even when I read it, I was
bothered by both groups

00:04:01.889 --> 00:04:03.410
were told which kind of pill they got.

00:04:03.410 --> 00:04:06.243
That completely defeats
the purpose of a placebo.

00:04:06.243 --> 00:04:08.507
The placebo effect is, hey,
I think I'm taking something

00:04:08.507 --> 00:04:10.237
good for me, and it's been
documented that when you

00:04:10.237 --> 00:04:11.595
think you're taking a pill that helps you,

00:04:11.595 --> 00:04:13.685
it often times does help you.

00:04:13.685 --> 00:04:17.124
And so when someone's coming
up with a new medicine,

00:04:17.124 --> 00:04:21.005
it better perform, or it
better be more effective

00:04:21.005 --> 00:04:22.922
than just that placebo.

00:04:23.895 --> 00:04:25.582
I don't like the fact that,

00:04:25.582 --> 00:04:28.397
this is a very bad study
to tell both groups

00:04:28.397 --> 00:04:29.964
what kind of pill they got.

00:04:29.964 --> 00:04:31.485
You actually should tell neither group

00:04:31.485 --> 00:04:33.818
which type of pill they got.


Worked example identifying sample study

WEBVTT
Kind: captions
Language: en

00:00:00.578 --> 00:00:02.383
- [Instructor] Let's take a
look at some statistical studies

00:00:02.383 --> 00:00:04.826
and see if we can figure
out what type they are.

00:00:04.826 --> 00:00:06.511
So this first one, "Roy's Toys received

00:00:06.511 --> 00:00:09.814
"a shipment of 100,000 rubber
duckies from the factory.

00:00:09.814 --> 00:00:12.191
"The factory couldn't promise
that all rubber duckies

00:00:12.191 --> 00:00:13.382
"are in perfect form,

00:00:13.382 --> 00:00:14.871
"but they promised that the percentage

00:00:14.871 --> 00:00:18.168
"of defective toys won't exceed 5%."

00:00:18.168 --> 00:00:19.692
Let me underline that.

00:00:19.692 --> 00:00:21.054
"They promised that the percentage

00:00:21.054 --> 00:00:24.055
"of defective toys won't exceed 5%.

00:00:24.055 --> 00:00:25.873
"Roy wanted to get an estimation

00:00:25.873 --> 00:00:27.967
"of the percentage of defective toys,

00:00:27.967 --> 00:00:32.591
"and since he couldn't go over
the entire 100,000 duckies,

00:00:32.591 --> 00:00:36.236
"he took a random sample of 10 duckies.

00:00:36.236 --> 00:00:40.741
"He found that 10% of
them were defective."

00:00:40.741 --> 00:00:42.175
So what's going on here?

00:00:42.175 --> 00:00:43.550
Roy gets a shipment.

00:00:43.550 --> 00:00:45.911
There's a 100,000 ducks in the shipment.

00:00:45.911 --> 00:00:48.107
He wants to figure out what percentage

00:00:48.107 --> 00:00:49.997
of them are defective.

00:00:49.997 --> 00:00:53.154
He can't look at all 100,000 ducks.

00:00:53.154 --> 00:00:54.505
It's not practical,

00:00:54.505 --> 00:00:56.677
so he samples 10 of them.

00:00:56.677 --> 00:00:58.135
One, two, three, four,

00:00:58.135 --> 00:01:01.223
five, six, seven, eight, nine, 10,

00:01:01.223 --> 00:01:04.641
and he finds that one out
of those 10 are defective,

00:01:04.641 --> 00:01:06.627
10% of the 10.

00:01:06.627 --> 00:01:07.464
So first of all,

00:01:07.464 --> 00:01:10.662
this is clearly a sample study.

00:01:10.662 --> 00:01:13.412
This is a sample study.

00:01:13.412 --> 00:01:14.737
How do we know that?

00:01:14.737 --> 00:01:18.847
Well, he is taking a sample
from a broader population

00:01:18.847 --> 00:01:20.705
in order to estimate a parameter,

00:01:20.705 --> 00:01:24.153
and the parameter is the
percentage of those 100,000 duckies

00:01:24.153 --> 00:01:26.307
that are actually defective.

00:01:26.307 --> 00:01:27.665
Now, the next question is

00:01:27.665 --> 00:01:29.841
is what kind of conclusion can he make.

00:01:29.841 --> 00:01:31.501
Roy, since he got the shipment,

00:01:31.501 --> 00:01:32.832
and he took a sample

00:01:32.832 --> 00:01:35.592
and he found that 10% of
the sample was defective,

00:01:35.592 --> 00:01:37.416
he might be all up in arms

00:01:37.416 --> 00:01:41.149
and say, "Oh, this toy
shipment from the factory,

00:01:41.149 --> 00:01:43.399
"they violated this promise

00:01:44.330 --> 00:01:46.047
"that the percentage of defective toys

00:01:46.047 --> 00:01:47.331
"won't exceed 5%

00:01:47.331 --> 00:01:48.981
"'cause I sampled 10 toys,

00:01:48.981 --> 00:01:52.458
"and 10% of those 10 toys were defective."

00:01:52.458 --> 00:01:55.018
Well, that isn't a reasonable conclusion

00:01:55.018 --> 00:01:57.282
because this is a small sample.

00:01:57.282 --> 00:01:58.385
This is a small sample.

00:01:58.385 --> 00:01:59.218
Think about it.

00:01:59.218 --> 00:02:00.922
He could have sampled five duckies,

00:02:00.922 --> 00:02:03.070
and if he just happened to
get one of the defective ones,

00:02:03.070 --> 00:02:06.193
he would have said, "Oh,
maybe 20% are defective."

00:02:06.193 --> 00:02:08.248
What he's really gotta do is sample,

00:02:08.248 --> 00:02:10.105
take a larger sample,

00:02:10.105 --> 00:02:12.850
and, once again, whatever your sampling,

00:02:12.850 --> 00:02:13.994
there's always a probability

00:02:13.994 --> 00:02:16.751
that your estimate is going to be

00:02:16.751 --> 00:02:19.155
not close or definitely not the same

00:02:19.155 --> 00:02:21.703
as the parameter for the population.

00:02:21.703 --> 00:02:23.260
But the larger your sample,

00:02:23.260 --> 00:02:25.612
the higher a probability
that your estimate

00:02:25.612 --> 00:02:29.961
is close to the actual
parameter for the population,

00:02:29.961 --> 00:02:32.649
and 10 of this is just too low.

00:02:32.649 --> 00:02:33.498
In future videos,

00:02:33.498 --> 00:02:36.975
we'll talk about how you
can estimate the probability

00:02:36.975 --> 00:02:38.433
or how you can figure out

00:02:38.433 --> 00:02:41.600
whether your sample seemed sufficient.

00:02:42.960 --> 00:02:44.510
But for this one, for what Roy did,

00:02:44.510 --> 00:02:46.472
I don't think 10 duckies is enough.

00:02:46.472 --> 00:02:48.539
If he sampled maybe 100 duckies,

00:02:48.539 --> 00:02:49.495
or more than that,

00:02:49.495 --> 00:02:51.540
and he found that 10%
of them were defective,

00:02:51.540 --> 00:02:53.542
well, that seems less likely to happen

00:02:53.542 --> 00:02:56.213
just purely due to chance.

00:02:56.213 --> 00:02:57.519
Let's do a few more of these,

00:02:57.519 --> 00:02:59.858
and actually, I'll do
those in the next videos.


Identifying a sample and population

WEBVTT
Kind: captions
Language: en

00:00:00.175 --> 00:00:02.477
- [Narrator] Administrators
at Riverview High School

00:00:02.477 --> 00:00:06.869
surveyed a random sample
of 100 of their seniors

00:00:06.869 --> 00:00:09.809
to see how they felt
about the lunch offerings

00:00:09.809 --> 00:00:12.392
at the school's cafeteria.

00:00:12.392 --> 00:00:14.507
So you have all of the seniors,

00:00:14.507 --> 00:00:16.314
I'm assuming there's more
than a hundred of them,

00:00:16.314 --> 00:00:17.969
and then they sampled a hundred of them.

00:00:17.969 --> 00:00:19.621
So this is the sample.

00:00:19.621 --> 00:00:23.084
So the population is all of
the seniors at the school.

00:00:23.084 --> 00:00:25.479
That's the population, all of the seniors.

00:00:25.479 --> 00:00:27.467
And they sampled a hundred of them.

00:00:27.467 --> 00:00:29.203
So the hundred seniors that the talked to,

00:00:29.203 --> 00:00:30.772
that is the sample.

00:00:30.772 --> 00:00:32.355
That is the sample.

00:00:33.660 --> 00:00:35.397
So they tell us, identify the population

00:00:35.397 --> 00:00:36.712
and the sample this setting.

00:00:36.712 --> 00:00:38.083
So let's just see which if these choices

00:00:38.083 --> 00:00:39.486
actually match up to what I just said.

00:00:39.486 --> 00:00:41.720
And like always, I encourage
you to pause the video

00:00:41.720 --> 00:00:43.661
and see if you can work
through it on your own.

00:00:43.661 --> 00:00:47.887
So, the population is all high
school seniors in the world;

00:00:47.887 --> 00:00:52.054
the sample is all of the
seniors at Riverview High.

00:00:53.425 --> 00:00:55.078
No, this is not right.

00:00:55.078 --> 00:00:58.413
We're not trying to get an indication of

00:00:58.413 --> 00:01:00.760
how all of the high school
seniors in the world

00:01:00.760 --> 00:01:04.078
feel about the food at
Riverview High School.

00:01:04.078 --> 00:01:04.926
We're trying to get an indication of

00:01:04.926 --> 00:01:07.406
how the seniors at Riverview High School

00:01:07.406 --> 00:01:11.323
feel about the lunch at
the school's cafeteria.

00:01:12.267 --> 00:01:15.031
So they did a sample of hundred of them.

00:01:15.031 --> 00:01:17.882
So this is definitely not going to be--

00:01:17.882 --> 00:01:20.049
let me cross this one out.

00:01:21.936 --> 00:01:25.085
The population is all
students at Riverview High;

00:01:25.085 --> 00:01:29.597
the sample is all of the
seniors at Riverview High.

00:01:29.597 --> 00:01:32.142
Well, they clearly didn't
sample all of the seniors,

00:01:32.142 --> 00:01:34.629
they sampled a hundred of the seniors.

00:01:34.629 --> 00:01:36.535
So this isn't gonna be right either.

00:01:36.535 --> 00:01:40.551
Let's hope that the third
choice is working out.

00:01:40.551 --> 00:01:43.822
The population is all
seniors at Riverview High;

00:01:43.822 --> 00:01:46.488
the sample is the
hundred seniors surveyed.

00:01:46.488 --> 00:01:48.526
Yep, that's exactly what we talked here.

00:01:48.526 --> 00:01:50.298
We're trying to get an
indication about how

00:01:50.298 --> 00:01:52.831
all of the seniors at Riverview High feel

00:01:52.831 --> 00:01:55.107
about the food, the lunch offerings.

00:01:55.107 --> 00:01:57.255
We probably think it's impractical

00:01:57.255 --> 00:01:58.698
or the administrators
feel it's impractical

00:01:58.698 --> 00:01:59.976
to talk to everyone.

00:01:59.976 --> 00:02:02.357
So, to get exactly what
the population thinks.

00:02:02.357 --> 00:02:03.190
So instead they're gonna do

00:02:03.190 --> 00:02:04.773
a random sample of a hundred of them.

00:02:04.773 --> 00:02:07.271
So the sample is the hundred seniors

00:02:07.271 --> 00:02:09.438
who are actually surveyed.


Ancient history and the Old Testament

WEBVTT
Kind: captions
Language: en

00:00:00.178 --> 00:00:01.638
- [Instructor] In the next
few videos, we're gonna do

00:00:01.638 --> 00:00:05.038
a very high-level overview
of ancient history.

00:00:05.038 --> 00:00:08.038
We're literally going to try
to cover 3,000 years of history

00:00:08.038 --> 00:00:10.306
in a handful of videos.

00:00:10.306 --> 00:00:11.687
And we're going to focus on

00:00:11.687 --> 00:00:13.591
not all of the history in the world,

00:00:13.591 --> 00:00:15.718
and it's worth noting that
there's going to be history

00:00:15.718 --> 00:00:18.789
in North and South America
that we're not gonna talk about

00:00:18.789 --> 00:00:21.675
in this video, history in
Africa or parts of Africa

00:00:21.675 --> 00:00:23.224
that we're not going to
talk about in this video,

00:00:23.224 --> 00:00:25.633
parts of Europe, parts of the Far East,

00:00:25.633 --> 00:00:28.193
but we're gonna focus
mainly on the Middle East.

00:00:28.193 --> 00:00:30.384
So, this yellow line, this is going to be

00:00:30.384 --> 00:00:34.473
the historical timeline for ancient Egypt.

00:00:34.473 --> 00:00:36.553
So, that is Egypt.

00:00:36.553 --> 00:00:40.206
We're talking about that
area right over there.

00:00:40.206 --> 00:00:42.422
This green timeline right over here,

00:00:42.422 --> 00:00:44.604
you could view this as Judea.

00:00:44.604 --> 00:00:47.127
We could also include,
we could include Judea,

00:00:47.127 --> 00:00:50.295
which is where modern-day
Israel, Palestine.

00:00:50.295 --> 00:00:51.508
If we're talking about Lebanon,

00:00:51.508 --> 00:00:53.526
then we could include
Phoenicia right over there.

00:00:53.526 --> 00:00:55.701
So, I'll just write Judea.

00:00:55.701 --> 00:00:57.099
Judea.

00:00:57.099 --> 00:00:59.045
That's a little hard to read.

00:00:59.045 --> 00:00:59.878
Judea.

00:01:02.172 --> 00:01:04.625
This blue and red and
white line right over here,

00:01:04.625 --> 00:01:07.151
this will be Mesopotamia.

00:01:07.151 --> 00:01:10.064
So, this will be Mesopotamia.

00:01:10.064 --> 00:01:12.254
We talk a little bit about
that in another video,

00:01:12.254 --> 00:01:15.989
the area between or around the
Tigris and Euphrates River,

00:01:15.989 --> 00:01:19.942
mainly in modern-day Iraq,
that region right over there.

00:01:19.942 --> 00:01:24.838
So, if we say this is
Judea, this is Mesopotamia.

00:01:24.838 --> 00:01:28.564
And we will also touch a
little bit later on Persia,

00:01:28.564 --> 00:01:30.385
which is that area there.

00:01:30.385 --> 00:01:32.631
The ancient Greece.

00:01:32.631 --> 00:01:36.098
We're gonna talk a little bit
about Rome, about Carthage,

00:01:36.098 --> 00:01:39.388
and a little bit about the Indus Valley,

00:01:39.388 --> 00:01:41.926
the Indus Valley civilizations,

00:01:41.926 --> 00:01:45.248
which is in now modern-day Pakistan.

00:01:45.248 --> 00:01:47.335
So, with that geographic bearing,

00:01:47.335 --> 00:01:51.502
and this is a line for, I'll
call this the Indus Valley.

00:01:52.393 --> 00:01:55.560
Or Indus Valley, or I could say India.

00:01:56.678 --> 00:01:59.024
And, obviously, India's much more

00:01:59.024 --> 00:02:00.116
than just the Indus Valley.

00:02:00.116 --> 00:02:01.668
And even this line over
here, that's China,

00:02:01.668 --> 00:02:04.886
although I'm not gonna go much
into that into this video.

00:02:04.886 --> 00:02:08.053
So, let's start about 5,000 years ago.

00:02:09.208 --> 00:02:11.068
And there were already people

00:02:11.068 --> 00:02:12.755
in all of the regions that I talk about,

00:02:12.755 --> 00:02:14.024
but where I'm really highlighting

00:02:14.024 --> 00:02:17.791
is where you start to have
significant empires develop.

00:02:17.791 --> 00:02:21.002
And I'll start in Egypt,
where you have the Pharaoh,

00:02:21.002 --> 00:02:22.307
you have King Menes.

00:02:22.307 --> 00:02:24.667
He's able to unify Upper and Lower Egypt.

00:02:24.667 --> 00:02:26.229
And it's a little counter-intuitive;

00:02:26.229 --> 00:02:29.182
Upper Egypt is actually
south of Lower Egypt,

00:02:29.182 --> 00:02:32.028
and that's because the Nile
River forms further South

00:02:32.028 --> 00:02:34.045
and then it flows northward.

00:02:34.045 --> 00:02:38.024
And so, he's able to unify
Upper and Lower Egypt,

00:02:38.024 --> 00:02:42.847
Upper's off the map here,
into the Egyptian civilization

00:02:42.847 --> 00:02:44.931
that we often associate
with ancient Egypt.

00:02:44.931 --> 00:02:48.538
And it is a very, very, very
long-lived civilization.

00:02:48.538 --> 00:02:50.537
And this is right at around the same time

00:02:50.537 --> 00:02:54.621
you have folks living in
Mesopotamia, you have the Acadians.

00:02:54.621 --> 00:02:56.248
We're going into deep history now,

00:02:56.248 --> 00:02:58.370
so we don't know exactly what was going on

00:02:58.370 --> 00:02:59.651
in the Indus Valley,

00:02:59.651 --> 00:03:01.430
but we believe that there
were people there as well,

00:03:01.430 --> 00:03:03.603
and there were people
many, many other places.

00:03:03.603 --> 00:03:05.099
But now, let's fast-forward.

00:03:05.099 --> 00:03:07.511
We're gonna go about 500 years.

00:03:07.511 --> 00:03:11.193
And we associate ancient
Egypt with the pyramids,

00:03:11.193 --> 00:03:13.658
and, relatively speaking,
the pyramids were built

00:03:13.658 --> 00:03:17.641
fairly early in the
history of ancient Egypt.

00:03:17.641 --> 00:03:20.224
They were built about 2500 BCE,

00:03:21.862 --> 00:03:25.779
or so we're talking about
4,500-4,600 years ago

00:03:26.770 --> 00:03:29.029
was the time that,
especially the most famous,

00:03:29.029 --> 00:03:31.992
the Great Pyramid of Giza was built.

00:03:31.992 --> 00:03:34.764
But now, let's fast-forward even more.

00:03:34.764 --> 00:03:37.266
And in this narrative
that I'm gonna talk about

00:03:37.266 --> 00:03:40.223
in this video, I'm going to
introduce some characters

00:03:40.223 --> 00:03:41.577
from the Old Testament.

00:03:41.577 --> 00:03:43.268
And a lotta the stories
from the Old Testament,

00:03:43.268 --> 00:03:45.817
they have bases in
history, but some of them,

00:03:45.817 --> 00:03:47.802
the historical evidence isn't as clear.

00:03:47.802 --> 00:03:49.034
But I'm gonna include it

00:03:49.034 --> 00:03:52.852
because it's so much a
part of Judeo-Christian,

00:03:52.852 --> 00:03:56.594
Islamic, Western, and
not just Western culture

00:03:56.594 --> 00:03:58.440
that I think it gives us
an interesting bearing

00:03:58.440 --> 00:04:00.867
and, frankly, a lotta the
stories from the Old Testament

00:04:00.867 --> 00:04:03.188
are just really fascinating stories.

00:04:03.188 --> 00:04:07.248
So now, let's fast-forward to 1700 BCE.

00:04:07.248 --> 00:04:10.033
And even though that seems like
a very, very long time ago,

00:04:10.033 --> 00:04:12.798
and it was, it's nice to get our bearings.

00:04:12.798 --> 00:04:17.319
Because in 1700 BCE, and we're
gonna talk about Hammurabi,

00:04:17.319 --> 00:04:22.145
who lived in, who ruled over
Babylon, the Babylonian Empire.

00:04:22.145 --> 00:04:25.562
So, Hammurabi right over here in Babylon,

00:04:27.848 --> 00:04:30.253
famous for the Code of Hammurabi.

00:04:30.253 --> 00:04:33.408
He codified a series of laws.

00:04:33.408 --> 00:04:38.265
We believe Abraham grew up at
around the same time in Ur.

00:04:38.265 --> 00:04:42.683
And Abraham, we believe
that he was coincident

00:04:42.683 --> 00:04:44.940
or maybe came a little bit after Hammurabi

00:04:44.940 --> 00:04:46.285
because a lotta the Old Testament

00:04:46.285 --> 00:04:48.600
seems to at least be inspired

00:04:48.600 --> 00:04:51.627
by some of what we see
in Hammurabi's Code.

00:04:51.627 --> 00:04:52.731
And it's worth noting that

00:04:52.731 --> 00:04:54.518
even though this seems
like a long time ago,

00:04:54.518 --> 00:04:58.207
around 1700 BCE, the time of Hammurabi,

00:04:58.207 --> 00:04:59.350
and it turns a little white here

00:04:59.350 --> 00:05:01.149
'cause this is where the Babylonians

00:05:01.149 --> 00:05:05.316
become one of the dominant
empires in Mesopotamia,

00:05:06.194 --> 00:05:07.541
even though it seems like a long time ago,

00:05:07.541 --> 00:05:09.848
it's worth noting that
in the time of Hammurabi

00:05:09.848 --> 00:05:12.140
and in the time of Abraham,

00:05:12.140 --> 00:05:17.072
the pyramids were already close
to, what, 700-800 years old.

00:05:17.072 --> 00:05:19.454
So, they already would've
viewed the pyramids

00:05:19.454 --> 00:05:21.848
as a very, very old thing.

00:05:21.848 --> 00:05:25.721
But if we go with roughly the year 1700

00:05:25.721 --> 00:05:27.793
for about the time of Abraham,

00:05:27.793 --> 00:05:30.210
he goes to what becomes Judea

00:05:32.741 --> 00:05:35.991
and his tribe becomes established there

00:05:36.922 --> 00:05:39.208
and then ends up, that's
where I have this line

00:05:39.208 --> 00:05:41.875
going from Mesopotamia to Judea,

00:05:43.758 --> 00:05:46.595
and we kinda see the
established Judea here.

00:05:46.595 --> 00:05:48.005
And once again, they're
not the first people there.

00:05:48.005 --> 00:05:49.577
You had the Canaanites,
you had the Phoenicians,

00:05:49.577 --> 00:05:52.253
you had many other folks in this region,

00:05:52.253 --> 00:05:54.011
but this is interesting because it relates

00:05:54.011 --> 00:05:57.718
to the Old Testament that
many folks are familiar with.

00:05:57.718 --> 00:06:00.321
And then you have the Old,
so this is around 1700.

00:06:00.321 --> 00:06:01.154
Let me write this.

00:06:01.154 --> 00:06:02.571
This is 1700 BCE.

00:06:03.967 --> 00:06:07.052
And once again, I'll put approximately.

00:06:07.052 --> 00:06:08.861
And then you go a few hundred years

00:06:08.861 --> 00:06:11.991
and there isn't a lot of
historical basis here,

00:06:11.991 --> 00:06:15.143
but based on the Old
Testament, the story of Joseph,

00:06:15.143 --> 00:06:17.722
Joseph going to Egypt
and becoming the vizier

00:06:17.722 --> 00:06:20.565
for the Pharaoh and
then his family coming,

00:06:20.565 --> 00:06:22.749
Joseph, who is Abraham's grandson,

00:06:22.749 --> 00:06:26.916
according to the Old Testament,
that story happens around,

00:06:28.717 --> 00:06:31.321
and once again, this is not
established well historically

00:06:31.321 --> 00:06:32.616
and I encourage you to look up

00:06:32.616 --> 00:06:34.570
and see if you can find evidence yourself

00:06:34.570 --> 00:06:38.737
of when folks think this
is, but the best evidence,

00:06:39.654 --> 00:06:41.885
if this happened, the
best evidence I've seen

00:06:41.885 --> 00:06:46.245
is it's around 1500
BCE, the story of Joseph

00:06:46.245 --> 00:06:48.694
and the migration of the Jewish people,

00:06:48.694 --> 00:06:50.322
or a significant chunk
of the Jewish people,

00:06:50.322 --> 00:06:53.477
to Egypt, where they get enslaved.

00:06:53.477 --> 00:06:55.964
And that enslavement is from 1500,

00:06:55.964 --> 00:06:58.605
they get enslaved by the Pharaoh, 1500,

00:06:58.605 --> 00:07:00.772
to approximately 1200 BCE.

00:07:02.748 --> 00:07:04.389
And so, that story, you go all the way

00:07:04.389 --> 00:07:08.556
from Abraham through Joseph,
this is in the Old Testament,

00:07:09.402 --> 00:07:11.401
this happens in Genesis,

00:07:11.401 --> 00:07:13.032
and then we get to the story of Moses,

00:07:13.032 --> 00:07:16.342
where he sets his people
free, the story of Exodus

00:07:16.342 --> 00:07:20.969
and the Jews are able
to go back to Jerusalem.

00:07:20.969 --> 00:07:22.896
Moses leads them, parts the Red Sea,

00:07:22.896 --> 00:07:24.995
according to the Old Testament,

00:07:24.995 --> 00:07:28.076
and there established
the kingdom of Jerusalem

00:07:28.076 --> 00:07:29.483
where you have the famous kings,

00:07:29.483 --> 00:07:32.600
King Saul, then King
David, then King Solomon;

00:07:32.600 --> 00:07:36.790
Solomon, who famous for
building the first temple.

00:07:36.790 --> 00:07:39.306
And now, since I'm already
close to the 10-minute mark,

00:07:39.306 --> 00:07:41.767
I'll leave you there,
because we've already covered

00:07:41.767 --> 00:07:44.414
about 2,000 years of history (laughs)

00:07:44.414 --> 00:07:45.825
in about eight minutes.

00:07:45.825 --> 00:07:50.767
In the next video, we're
gonna go from about 1,000 BCE

00:07:50.767 --> 00:07:54.350
to the Roman Empire
and the birth of Jesus.


Dataset individuals and categorical variables

WEBVTT
Kind: captions
Language: en

00:00:00.322 --> 00:00:01.992
- [Instructor] So we have
this question that says

00:00:01.992 --> 00:00:05.170
millions of Americans rely
on caffeine to get them up

00:00:05.170 --> 00:00:06.388
in the morning.

00:00:06.388 --> 00:00:08.750
Now that is probably true,
although for me if I drink

00:00:08.750 --> 00:00:10.314
even a little bit of
caffeine in the morning,

00:00:10.314 --> 00:00:11.860
I won't be able to sleep that night.

00:00:11.860 --> 00:00:14.177
Here's nutritional data
on some popular drinks

00:00:14.177 --> 00:00:16.427
at Ben's Beans Coffee Shop.

00:00:17.640 --> 00:00:19.812
And so we have the names
of different drinks

00:00:19.812 --> 00:00:22.126
and for each of those drinks,
we know not only their name,

00:00:22.126 --> 00:00:23.958
we know whether they
are hot or cold, we know

00:00:23.958 --> 00:00:27.592
how many calories they contain,
sugar content, caffeine.

00:00:27.592 --> 00:00:30.391
Alright, now with that, let's
try to answer these questions.

00:00:30.391 --> 00:00:32.221
And like always, try to pause
the video and see if you

00:00:32.221 --> 00:00:34.370
can answer these questions before I do.

00:00:34.370 --> 00:00:36.801
And on this one on the right,
there's a little bit below

00:00:36.801 --> 00:00:40.654
what you see, there's a
little bit below, and actually

00:00:40.654 --> 00:00:41.956
I'll just leave it there.

00:00:41.956 --> 00:00:45.562
So the individuals in this data set are.

00:00:45.562 --> 00:00:48.645
So when you see the term
individuals, you might immediately

00:00:48.645 --> 00:00:50.627
say, oh, they must be
talking about people, because

00:00:50.627 --> 00:00:52.310
in every day language, we
talk about individuals,

00:00:52.310 --> 00:00:54.213
we tend to be talking about people.

00:00:54.213 --> 00:00:57.713
But in statistical language,
it's really referring to

00:00:57.713 --> 00:01:00.470
what are the different
entries of the data set

00:01:00.470 --> 00:01:01.569
referring to?

00:01:01.569 --> 00:01:04.914
So each of these rows of the
data set, they're not referring

00:01:04.914 --> 00:01:09.152
to types of people or customers
and things about those

00:01:09.152 --> 00:01:13.319
customers, that's not about
Ben's Beans' customers,

00:01:14.509 --> 00:01:17.550
I have trouble saying that,
each of these rows represent

00:01:17.550 --> 00:01:21.629
one of the drinks that
are offered at Ben's Beans

00:01:21.629 --> 00:01:23.176
Coffee Shop.

00:01:23.176 --> 00:01:25.328
So those are the individuals.

00:01:25.328 --> 00:01:28.885
In statistical language,
individuals could refer to things.

00:01:28.885 --> 00:01:32.509
What are the things
represented in that data set?

00:01:32.509 --> 00:01:34.826
Then we're asked, the data
set contains, and we're asked

00:01:34.826 --> 00:01:37.268
how many variables does it
contain, and whether or not

00:01:37.268 --> 00:01:39.712
they're categorical or not.

00:01:39.712 --> 00:01:41.598
So first, what's a variable?

00:01:41.598 --> 00:01:43.490
Well the variables are what
are the things that are

00:01:43.490 --> 00:01:45.736
different about these
different entries, about these

00:01:45.736 --> 00:01:48.955
different individuals that
this table tells us about,

00:01:48.955 --> 00:01:50.853
that this data set tells us about.

00:01:50.853 --> 00:01:55.185
Well, the name is variable,
each of these drinks

00:01:55.185 --> 00:01:57.759
have different names, that's one variable,

00:01:57.759 --> 00:02:00.307
the type is variable,
they can be hot or cold,

00:02:00.307 --> 00:02:02.738
the calories are variable,
the sugar content is variable

00:02:02.738 --> 00:02:03.850
and the caffeine is variable.

00:02:03.850 --> 00:02:06.718
So we have five variables over here.

00:02:06.718 --> 00:02:09.328
The name, type, calories, sugar, caffeine.

00:02:09.328 --> 00:02:11.835
So we have five variables,
so we can rule out

00:02:11.835 --> 00:02:14.467
the sixth variable
choices right over there,

00:02:14.467 --> 00:02:17.832
so we're gonna rule that
one, and that one out.

00:02:17.832 --> 00:02:21.167
And so how many of these are categorical?

00:02:21.167 --> 00:02:25.558
So a categorical variable,
lemme write this down,

00:02:25.558 --> 00:02:29.308
categorical variable
means that you're gonna,

00:02:30.470 --> 00:02:33.022
that dimension of the
thing you're measuring

00:02:33.022 --> 00:02:37.401
is gonna be placed into some
categories, or that variable

00:02:37.401 --> 00:02:41.228
takes on values that are
a name of a category.

00:02:41.228 --> 00:02:45.417
So for example, drink name,
this is a categorical variable

00:02:45.417 --> 00:02:49.682
because the values that
drink name can take on,

00:02:49.682 --> 00:02:52.495
well, those are the actual
names of the drinks.

00:02:52.495 --> 00:02:54.258
These are the categories,
it could be brewed coffee,

00:02:54.258 --> 00:02:58.549
cafe latte, cafe mocha,
cappuccino, so on and so forth.

00:02:58.549 --> 00:03:03.193
So this one right over here
is, this one over here is

00:03:03.193 --> 00:03:05.276
categorical, categorical.

00:03:06.199 --> 00:03:08.564
Now you might say what's
another type of variable?

00:03:08.564 --> 00:03:12.517
Well another type of
variable is quantitative.

00:03:12.517 --> 00:03:13.600
Quantitative.

00:03:15.965 --> 00:03:19.022
A quantitative variable is going
to take on numerical values

00:03:19.022 --> 00:03:20.633
as opposed to categorical values.

00:03:20.633 --> 00:03:24.888
Numerical values that are
measuring some type of attribute.

00:03:24.888 --> 00:03:26.451
So let's go through these.

00:03:26.451 --> 00:03:27.967
So what about type?

00:03:27.967 --> 00:03:30.607
Well type isn't a numerical
value, it's taking on

00:03:30.607 --> 00:03:33.040
a category, it's either
gonna be hot or cold.

00:03:33.040 --> 00:03:36.254
So that is also going to be categorical,

00:03:36.254 --> 00:03:39.419
but these other three, these
take on numerical values

00:03:39.419 --> 00:03:41.354
that represent in the case
of calories, the number

00:03:41.354 --> 00:03:45.030
of calories a serving has, the
number, the amount of sugar

00:03:45.030 --> 00:03:47.113
in the sugar variable,
or the amount of caffeine

00:03:47.113 --> 00:03:48.860
in the caffeine variable.

00:03:48.860 --> 00:03:53.027
So these three right over
here, these are quantitative

00:03:54.168 --> 00:03:55.068
variables.

00:03:55.068 --> 00:03:57.757
So you have five total
variables, two of which

00:03:57.757 --> 00:04:01.022
are categorical, and three
of which are quantitative.

00:04:01.022 --> 00:04:04.018
And so that is this choice
right here, five variables,

00:04:04.018 --> 00:04:05.854
two of which are categorical.

00:04:05.854 --> 00:04:07.913
The two categorical ones
are, well what name,

00:04:07.913 --> 00:04:09.453
what's the name of the
drink, and also whether

00:04:09.453 --> 00:04:11.203
they are hot or cold.


Rome becomes dominant

WEBVTT
Kind: captions
Language: en

00:00:00.772 --> 00:00:03.145
- [Instructor] Last video,
we end with the conquests

00:00:03.145 --> 00:00:06.369
of Alexander the Great,
how he's able to conquer

00:00:06.369 --> 00:00:10.029
most of the map that we see right up here,

00:00:10.029 --> 00:00:14.336
especially from Greece all the
way through the Middle East

00:00:14.336 --> 00:00:18.503
through Persia and getting
to the borders of India.

00:00:19.677 --> 00:00:22.264
Conquering Egypt as well.

00:00:22.264 --> 00:00:24.566
And we mentioned that he
had a very short life,

00:00:24.566 --> 00:00:27.887
and after his death, his empire was split.

00:00:27.887 --> 00:00:31.221
But coincident with that
time we have other powers

00:00:31.221 --> 00:00:34.301
in other parts of the
Mediterranean that are growing.

00:00:34.301 --> 00:00:38.877
Mainly the Romans, and Rome
was established several

00:00:38.877 --> 00:00:42.616
hundred years before the
time of Alexander the Great,

00:00:42.616 --> 00:00:46.154
but by that time they're
starting to conquer more and more

00:00:46.154 --> 00:00:49.234
of the Italian peninsula right over here,

00:00:49.234 --> 00:00:51.134
what we would now consider
the Italian peninsula.

00:00:51.134 --> 00:00:55.301
You also have a power in
Carthage forming right over here

00:00:56.726 --> 00:00:58.829
on the north African coast.

00:00:58.829 --> 00:01:02.746
Carthage was initially
a Phoenician settlement.

00:01:04.309 --> 00:01:07.266
The Phoenicians, famous
for our phonetic alphabet.

00:01:07.266 --> 00:01:09.940
The Phoenicians were based in what is now

00:01:09.940 --> 00:01:14.033
modern day Lebanon and
Syria and they were traders

00:01:14.033 --> 00:01:16.605
and they settled throughout
the Mediterranean.

00:01:16.605 --> 00:01:20.772
And Carthage and the Carthaginian
power or civilization

00:01:21.844 --> 00:01:24.475
was a fairly significant one at this time.

00:01:24.475 --> 00:01:26.646
And you could imagine
when there are two powers

00:01:26.646 --> 00:01:28.470
that are close to each
other, they might want to be

00:01:28.470 --> 00:01:31.119
going after the same interests
or conquest the same land,

00:01:31.119 --> 00:01:32.995
or they might view each other as a threat,

00:01:32.995 --> 00:01:35.277
they tend to start fighting each other.

00:01:35.277 --> 00:01:38.517
And that is true of Rome and Carthage.

00:01:38.517 --> 00:01:40.297
And the wars that they fought,

00:01:40.297 --> 00:01:42.928
they were called the Punic
Wars, and they occurred

00:01:42.928 --> 00:01:45.261
between 264 BCE and 146 BCE.

00:01:50.518 --> 00:01:53.101
You have the Punic Wars, Punic.

00:01:54.615 --> 00:01:57.238
And once again, we will do
many videos on the Punic Wars,

00:01:57.238 --> 00:02:00.694
but these are the wars that
involved Hannibal famously

00:02:00.694 --> 00:02:05.220
taking elephants across the
Alps in order to attack Rome.

00:02:05.220 --> 00:02:08.461
And we see it right over
here on our little timeline.

00:02:08.461 --> 00:02:11.914
The Punic Wars between Carthage and Rome.

00:02:11.914 --> 00:02:16.234
And just to be clear, this
little purple shaded in

00:02:16.234 --> 00:02:19.590
on our timeline, that
represents the Carthage

00:02:19.590 --> 00:02:22.421
and the Carthaginian civilization.

00:02:22.421 --> 00:02:24.345
I guess you could say power.

00:02:24.345 --> 00:02:26.345
And this is Rome.

00:02:26.345 --> 00:02:30.260
But eventually Rome wins
decisively and is actually able

00:02:30.260 --> 00:02:34.454
to wipe out Carthage,
literally wipe it out.

00:02:34.454 --> 00:02:38.621
And so Rome more and more
is becoming the dominant

00:02:39.493 --> 00:02:43.143
power in the Mediterranean,
and as you will see shortly,

00:02:43.143 --> 00:02:45.185
well beyond the Mediterranean.

00:02:45.185 --> 00:02:48.594
In this period Rome is a republic.

00:02:48.594 --> 00:02:50.957
And when we talk about a
republic, we're talking about

00:02:50.957 --> 00:02:55.124
some type of representation,
although it's not the same form

00:02:56.326 --> 00:02:58.575
of representation that
we would think today.

00:02:58.575 --> 00:03:02.649
We would be talking some
form of preserving rights.

00:03:02.649 --> 00:03:05.564
Once again, not exactly
as what we would do today.

00:03:05.564 --> 00:03:10.384
But as we get to the middle
of the first century BCE,

00:03:10.384 --> 00:03:13.206
we have a very successful Roman general,

00:03:13.206 --> 00:03:16.207
you might have heard
his name, Julius Caesar,

00:03:16.207 --> 00:03:19.252
who was able to take power of Rome,

00:03:19.252 --> 00:03:22.626
who was able to be the dictator of Rome.

00:03:22.626 --> 00:03:26.209
And in power he starts
to end the republic,

00:03:27.731 --> 00:03:29.105
and he does many other things.

00:03:29.105 --> 00:03:30.835
He starts rearranging the calendar,

00:03:30.835 --> 00:03:33.252
gets a month named after him, July,

00:03:33.252 --> 00:03:38.033
and then he gets assassinated
and his adopted son Augustus

00:03:38.033 --> 00:03:39.502
takes power.

00:03:39.502 --> 00:03:43.511
And Augustus is often
considered to be the first true

00:03:43.511 --> 00:03:47.288
emperor of Rome, that
the republic is now over.

00:03:47.288 --> 00:03:51.179
And he, too, gets a month
named after him, August.

00:03:51.179 --> 00:03:54.165
Now at around the same
time, you have another

00:03:54.165 --> 00:03:57.229
very significant figure that shows up,

00:03:57.229 --> 00:04:01.237
and obviously Jesus is a
significant figure in religion,

00:04:01.237 --> 00:04:03.439
but that has effect on history.

00:04:03.439 --> 00:04:05.869
His teachings are the basis of a religion

00:04:05.869 --> 00:04:07.930
that's going to have profound consequences

00:04:07.930 --> 00:04:11.053
for the rest of history, for the world.

00:04:11.053 --> 00:04:14.125
And even though we might
have this, you could say,

00:04:14.125 --> 00:04:16.025
the secular timeline of BCE,

00:04:16.025 --> 00:04:20.006
refers to before the common
era, or CE, the common era.

00:04:20.006 --> 00:04:23.231
Right now the year I'm
making this video is 2016 CE.

00:04:23.231 --> 00:04:25.907
Those are secular versions
of a calendar which is,

00:04:25.907 --> 00:04:27.929
essentially, centered on Jesus' birth

00:04:27.929 --> 00:04:30.277
or pretty close to Jesus' birth.

00:04:30.277 --> 00:04:32.839
Instead of BC, it would be before Christ.

00:04:32.839 --> 00:04:37.660
Instead of CE, we say AD, in
the year of our Lord in Latin.

00:04:37.660 --> 00:04:40.855
And Jesus, it's important
to note, because sometimes

00:04:40.855 --> 00:04:42.995
people might learn about the life of Jesus

00:04:42.995 --> 00:04:44.980
and they might think about the holy land

00:04:44.980 --> 00:04:47.721
and Nazareth and Jerusalem
and whatever else,

00:04:47.721 --> 00:04:50.697
but it's important to
realize that at that time,

00:04:50.697 --> 00:04:54.053
Jesus was born and raised
and grew up and died

00:04:54.053 --> 00:04:56.419
as part of the Roman Empire.

00:04:56.419 --> 00:04:59.821
In fact, when he was born,
which wasn't actually zero BCE,

00:04:59.821 --> 00:05:03.598
or zero we should just say,
year zero, we now think

00:05:03.598 --> 00:05:06.681
it was pretty close to 4 BCE or 4 BC.

00:05:07.731 --> 00:05:11.215
So he was born in the reign of Augustus

00:05:11.215 --> 00:05:15.047
and he died in the reign of Tiberius.

00:05:15.047 --> 00:05:18.178
So some of the first Roman emperors.

00:05:18.178 --> 00:05:20.339
So with that, we have our overview

00:05:20.339 --> 00:05:23.018
of especially ancient civilization.

00:05:23.018 --> 00:05:25.353
And in the next few
videos, or many vidoes,

00:05:25.353 --> 00:05:28.536
we will go deeper and drill
deep into each of these topics

00:05:28.536 --> 00:05:30.606
that we referred to, but
hopefully the last few videos

00:05:30.606 --> 00:05:33.684
give you a high-level
view of what was happening

00:05:33.684 --> 00:05:36.871
in the world both in
terms of time and space


Ancient Greeks and Persians

WEBVTT
Kind: captions
Language: en

00:00:00.305 --> 00:00:02.486
- [Narrator] So where we
left off in the last video,

00:00:02.486 --> 00:00:05.562
we have the Neo-Babylonians,
the Chaldean Empire,

00:00:05.562 --> 00:00:09.376
being conquered by the Persians
led by Cyrus the Great.

00:00:09.376 --> 00:00:13.543
That's in 539 that Cyrus
the Great conquers Babylon.

00:00:14.827 --> 00:00:18.994
And they're able to establish
a significant empire.

00:00:20.035 --> 00:00:22.618
And his successor, Cambyses II,

00:00:23.964 --> 00:00:27.622
in 525 B.C.E., is able to do something

00:00:27.622 --> 00:00:28.768
that many of the conquerors

00:00:28.768 --> 00:00:30.195
that we've talked about
in the last two videos

00:00:30.195 --> 00:00:33.271
were not able to do, and conquers Egypt,

00:00:33.271 --> 00:00:36.856
and makes that part of the Persian Empire.

00:00:36.856 --> 00:00:38.314
And so the Persian Empire

00:00:38.314 --> 00:00:40.613
is this vast and significant empire

00:00:40.613 --> 00:00:43.042
that gets created in this time period.

00:00:43.042 --> 00:00:46.399
But they soon run up to
a, I guess you could say

00:00:46.399 --> 00:00:48.464
a group of city-states, a civilization,

00:00:48.464 --> 00:00:51.335
that is able to fend off the Persians.

00:00:51.335 --> 00:00:53.419
And so just to be clear
what we're talking about

00:00:53.419 --> 00:00:55.299
or what I just talked about,

00:00:55.299 --> 00:00:58.132
here we are in roughly 539 B.C.E.,

00:01:01.700 --> 00:01:04.200
where you have Cyrus the Great

00:01:05.695 --> 00:01:09.206
is able to take over the
Neo-Babylonian Empire,

00:01:09.206 --> 00:01:11.589
and in doing so, going back to

00:01:11.589 --> 00:01:13.049
references from the Old Testament,

00:01:13.049 --> 00:01:16.128
ends the Babylonian captivity
of the Jewish people,

00:01:16.128 --> 00:01:17.834
is able to free the Jewish people,

00:01:17.834 --> 00:01:21.834
and then his successor,
Cambyses, in 525 B.C.E.,

00:01:23.567 --> 00:01:25.683
is able to take over Egypt.

00:01:25.683 --> 00:01:28.827
And so you see this expansion
of the Persian Empire

00:01:28.827 --> 00:01:30.823
on this timeline as well,

00:01:30.823 --> 00:01:34.343
but then they run up
against the Ancient Greeks.

00:01:34.343 --> 00:01:38.511
And the Ancient Greeks
were not a unified nation.

00:01:38.511 --> 00:01:40.508
They were a group of city-states.

00:01:40.508 --> 00:01:43.186
When people talk about city-states,
it's cities like Athens

00:01:43.186 --> 00:01:46.341
that are associated with the Ionians,

00:01:46.341 --> 00:01:49.590
that settled several hundred
years ago in that region,

00:01:49.590 --> 00:01:51.512
the Spartans, associated with the Dorians,

00:01:51.512 --> 00:01:54.022
who also moved down several hundred years

00:01:54.022 --> 00:01:56.231
before the time period
we're talking about.

00:01:56.231 --> 00:01:59.142
They each had, each
city was its own state,

00:01:59.142 --> 00:02:02.509
it had its own military, it had
its own laws and traditions.

00:02:02.509 --> 00:02:04.107
But they also had things in common.

00:02:04.107 --> 00:02:07.769
They shared a language,
they shared traditions,

00:02:07.769 --> 00:02:09.519
they shared religion.

00:02:10.500 --> 00:02:13.427
And the Persians, and I'll
do many videos on this,

00:02:13.427 --> 00:02:14.602
because it is fascinating,

00:02:14.602 --> 00:02:16.640
were not able to conquer,

00:02:16.640 --> 00:02:19.430
and now this is under the
successors of Cyrus the Great,

00:02:19.430 --> 00:02:22.181
or the successor of Cyrus
the Great and Cambyses,

00:02:22.181 --> 00:02:24.485
we have Darius the Great,

00:02:24.485 --> 00:02:26.742
is not able, in multiple attempts,

00:02:26.742 --> 00:02:28.683
to take over the Greeks,

00:02:28.683 --> 00:02:31.869
and these are the famous
Greco-Persian Wars,

00:02:31.869 --> 00:02:34.119
which occur from 499 B.C.E.

00:02:36.739 --> 00:02:37.906
to 450 B.C.E..

00:02:41.301 --> 00:02:42.884
Greco-Persian Wars.

00:02:47.470 --> 00:02:50.485
In our map, we see it right over here.

00:02:50.485 --> 00:02:52.313
The Greco-Persian Wars,

00:02:52.313 --> 00:02:53.993
and there are some famous instances,

00:02:53.993 --> 00:02:57.743
famous events, from why
we now run a marathon

00:02:59.037 --> 00:03:02.265
to the movie 300, where you have the stand

00:03:02.265 --> 00:03:06.742
that the Spartans attempted
to take at Thermopoli,

00:03:06.742 --> 00:03:08.643
where the Persian army,

00:03:08.643 --> 00:03:10.468
with thousands and thousands of soldiers

00:03:10.468 --> 00:03:14.389
facing 300 Spartans and
several hundred other soldiers,

00:03:14.389 --> 00:03:16.103
say "give us your arms,"

00:03:16.103 --> 00:03:18.040
and once again, we don't know
how much of this is legend

00:03:18.040 --> 00:03:19.767
and how much is true,
but it's a good story,

00:03:19.767 --> 00:03:21.667
and the Spartans say "come and take them,"

00:03:21.667 --> 00:03:22.876
and then the Persians say

00:03:22.876 --> 00:03:25.489
"we have enough arrows
to blot out the sun,"

00:03:25.489 --> 00:03:26.332
and the Spartans say

00:03:26.332 --> 00:03:29.009
"good, then we will fight in the shade."

00:03:29.009 --> 00:03:32.423
So the Greco-Persian Wars, they happened,

00:03:32.423 --> 00:03:34.484
and they're also the stuff of legend

00:03:34.484 --> 00:03:37.106
and some great stories
and great movies even

00:03:37.106 --> 00:03:39.959
have come out of those wars.

00:03:39.959 --> 00:03:43.013
Now, even though the city-states of Greece

00:03:43.013 --> 00:03:46.014
were able to fend off the
Persians in this time period,

00:03:46.014 --> 00:03:47.753
they're not able to unify,

00:03:47.753 --> 00:03:49.901
and in fact they start
fighting each other.

00:03:49.901 --> 00:03:52.647
And that's when you start
having the Peloponnesian Wars.

00:03:52.647 --> 00:03:53.480
So 431,

00:03:54.952 --> 00:03:56.202
from 431 B.C.E.

00:03:59.632 --> 00:04:00.799
to 404 B.C.E.,

00:04:03.153 --> 00:04:03.986
you have

00:04:05.684 --> 00:04:07.184
the Peloponnesian,

00:04:11.309 --> 00:04:13.326
or I should say the Peloponnesian War,

00:04:13.326 --> 00:04:16.567
and that is between Sparta and its allies

00:04:16.567 --> 00:04:18.726
and Athens and its allies,

00:04:18.726 --> 00:04:21.480
and we'll once again
do more videos on this,

00:04:21.480 --> 00:04:24.244
but Sparta is this militaristic society,

00:04:24.244 --> 00:04:26.937
Athens, famous for its
culture and its learning,

00:04:26.937 --> 00:04:29.633
but they're able to give
each other a good fight.

00:04:29.633 --> 00:04:32.050
Now can you imagine, while
they're fighting each other,

00:04:32.050 --> 00:04:35.559
it leaves them open to be
conquered by someone else.

00:04:35.559 --> 00:04:38.958
And that someone else ends
up being Philip of Macedon.

00:04:38.958 --> 00:04:42.030
And the Macedonians are a related people.

00:04:42.030 --> 00:04:44.370
They share a similar language,

00:04:44.370 --> 00:04:46.033
they have similar traditions,

00:04:46.033 --> 00:04:49.544
and Philip of Macedon is able to take over

00:04:49.544 --> 00:04:53.171
most of what we now consider to be Greece.

00:04:53.171 --> 00:04:56.338
And as famous as Philip of Macedon is,

00:04:57.446 --> 00:05:00.613
his son is perhaps one of the top five

00:05:01.465 --> 00:05:03.485
most famous people in history,

00:05:03.485 --> 00:05:07.421
because in 336, Philip of Macedon dies,

00:05:07.421 --> 00:05:09.760
he's actually assassinated,

00:05:09.760 --> 00:05:13.440
and his son, Alexander
the Great, takes over.

00:05:13.440 --> 00:05:15.037
And his son is only 20 years old.

00:05:15.037 --> 00:05:16.742
And he wasn't called Alexander
the Great when it started,

00:05:16.742 --> 00:05:21.105
but when he was done, they
called him Alexander the Great.

00:05:21.105 --> 00:05:23.156
So Alexander takes over,
I'll write this down,

00:05:23.156 --> 00:05:24.656
he's 20 years old,

00:05:26.082 --> 00:05:28.927
and he quickly consolidates power,

00:05:28.927 --> 00:05:33.783
not only over Greece, but he
starts establishing an empire,

00:05:33.783 --> 00:05:35.455
where he's able to take over,

00:05:35.455 --> 00:05:37.454
now the shoe's on the other foot,

00:05:37.454 --> 00:05:41.621
he's able to take over much of
what was the Persian Empire.

00:05:43.015 --> 00:05:45.378
He actually is able to take his armies

00:05:45.378 --> 00:05:48.248
all the way to the borders of India,

00:05:48.248 --> 00:05:51.563
he's taking over Egypt,
much of the Middle East.

00:05:51.563 --> 00:05:53.535
And so this is often referred to

00:05:53.535 --> 00:05:54.822
as the world's greatest adventure,

00:05:54.822 --> 00:05:56.641
and it's fascinating because he does this

00:05:56.641 --> 00:05:59.032
over a little bit more than a decade.

00:05:59.032 --> 00:06:01.532
He dies in 323, so 323 B.C.E.,

00:06:07.324 --> 00:06:08.794
Alexander dies.

00:06:08.794 --> 00:06:13.285
So he's in his early 30s,
and he managed to do this.

00:06:13.285 --> 00:06:14.548
Depending on what side
of history you're on,

00:06:14.548 --> 00:06:16.093
fortunately or unfortunately,

00:06:16.093 --> 00:06:17.919
his empire isn't able to last.

00:06:17.919 --> 00:06:19.258
So you can see this on our timeline.

00:06:19.258 --> 00:06:20.997
Alexander, in that brief, you know,

00:06:20.997 --> 00:06:21.926
a little bit more than a decade,

00:06:21.926 --> 00:06:25.207
he's able to take over
all of this territory.

00:06:25.207 --> 00:06:29.374
He's able to get his armies
to the borders of India,

00:06:30.908 --> 00:06:33.188
but then because he
dies, it's short-lived,

00:06:33.188 --> 00:06:34.398
the empire does not last.

00:06:34.398 --> 00:06:37.193
It actually gets split
into multiple empires.

00:06:37.193 --> 00:06:40.370
People who are related or
the generals of Alexander.

00:06:40.370 --> 00:06:42.269
We'll do many videos about that split.

00:06:42.269 --> 00:06:44.385
But maybe more interestingly,

00:06:44.385 --> 00:06:47.538
while that is happening,
at the time of Alexander,

00:06:47.538 --> 00:06:50.378
another group is emerging

00:06:50.378 --> 00:06:54.673
that will eventually turn into
one of the greatest empires

00:06:54.673 --> 00:06:56.965
that the world has ever seen.

00:06:56.965 --> 00:07:00.306
And that is the Romans, that we see,

00:07:00.306 --> 00:07:05.023
or they are the Romans, that
we will see in the next video.

00:07:05.023 --> 00:07:08.869
You can see it on this
timeline right over here.