So defining the population is far and away the most important task.

If you can't coherently describe the population,

then you can't really make any inferences.

Okay because there's nothing to make inference to.

So make sure that you can accurately characterize your population.

It's gonna help you tell the story better in the end after you analyze the data.

So similarly it's important that you identify an important question that you

wanna ask.

Just get an answer.

But presumably you've done this already.

[COUGH] So just as a very quick,

simple running example, here's a basic population of ten penguins.

Okay, and each of these penguins has a turquoise hat or a purple hat.

And so the basic question that we wanna ask is,

what proportion of this population of penguins is wearing a turquoise hat?

So, that's basically it.

Now, the problem, of course,

is that we can't collect data on the population, right?

Cuz they're penguins after all, and we can't take care of all of them, so

we need to draw a sample from this population.

So the sampling process is the manner in which the data come to you, and

this gives you the dataset.

So, let's say we're going to sample three penguins,

and the way that we sample those three penguins is, we stand there in

front of these ten penguins and we take the first three that walk up to us, okay?

So then we have our dataset of just three penguins.

And so, now the model for the population describes how the features of

the units in the population are related to each other, okay?

And this model can be more or

less complex depending on the type of question that we're trying to answer.

But the important thing is that the model connects the data that we observe

to the population that we don't observe.

All right?

And so, it's basically a little cartoon of how the world works.

So a couple of example of what a model might consist of is that we might assume,

for example, that the units of the population are independent of each other.

So imagine that the different penguins in this population

are wearing different hats.

And the fact that one penguin is wearing a turquoise hat doesn't really influence

whether another penguin is gonna wear a purple hat or a turquoise hat.

So they're all kind of independent of each other.

That's one assumption that we'll make as part of our model.

Another intentional assumption that we might want to make is that

certain features of the population are linearly related.

So there are linear relationships between them.

And there are many other kinds of things that you might wanna assume about

a population if your question's a little bit more complex.

Now the question that we're asking here is very simple.

We just wanna know what's the proportion of penguins with turquoise hats.

So we don't have to make too many assumptions about the population in order

to order that question.

So given our little data set here of three penguins,