Original subtitles

Narrator: Before crunching any numbers

and making decisions, we should introduce

some key definitions.

The first step of every statistical analysis you perform

is determine whether the data you are dealing with

is a population or a sample.

A population is the collection of all items of interest

to our study and is usually denoted with an uppercase N.

The numbers we've obtained when using a population

are called parameters.

A sample is a subset of the population

and is denoted with a lowercase n.

And the numbers we've obtained when working

with a sample are called statistics.

Now you know why the field we are studying

is called statistics.

Let's say, we wanna perform a survey of the job prospects

of the students studying in the New York University.

What is the population?

You can simply walk into New York University

and find every student, right?

Well, surely that would not be the population

of NYU students.

The population of interest includes not only the students

on campus, but also the ones at home, on exchange,

abroad, distant education students, part-time students,

even the ones who enrolled but are still in high school.

Though exhaustive, even this list misses someone.

Point taken.

Populations are hard to define

and hard to observe in real life.

A sample, however, is much easier to gather.

It is less time consuming and less costly.

Time and resources are the main reasons

we prefer drawing samples compared to analyzing

an entire population.

So, let's draw a sample then.

As we first wanted to do, we can just go to the NYU campus.

Next, let's enter the canteen

because we know it will be full of people.

We can then interview 50 of them.

Cool!

This is a sample drawn from the population of NYU students.

Good job!

Populations are hard to observe and contact.

That's why statistical tests are designed to work

with incomplete data.

You will almost always be working with sample data

and make data-driven decisions and inferences based on it.

All right.

Since statistical tests are usually based on sample data,

samples are key to accurate statistical insights.

They have two defining characteristics,

randomness and representativeness.

A sample must be both random and representative

for an insight to be precise.

A random sample is collected when each member

of the sample is chosen from the population

strictly by chance.

A representative sample is a subset of the population

that accurately reflects the members

of the entire population.

Let's go back to the sample we just discussed.

The 50 students from the NYU canteen.

We walked into the university canteen

and violated both conditions.

People were not chosen by chance.

They were a group of NYU students who were there for lunch.

Most members did not even get the chance to be chosen

as they were not in the canteen.

Thus, we conclude the sample was not random

but was it representative?

Well, it represented a group of people

but definitely not all students in the university.

To be exact, it represented the people who have lunch

at the university canteen.

Had our survey been about job prospects

of NYU students who eat in the university canteen,

we would've done well.

Okay.

You must be wondering how to draw a sample

that is both random and representative.

Well, the safest way would be to get access

to the student database

and contact individuals in a random manner.

However, such surveys are almost impossible to conduct

without assistance from the university.

All right.

Throughout the course, we will explore both sample

and population statistics.

After completing this course, samples and populations

will be a piece of cake for you.

Thanks for watching.