Course: Statistics for Business Analysis and Data Science

Transcript for this lesson

1 Understanding the Difference between A Population and A Sample

Slide 1

Before processing any data making decisions

1B

we should introduce some key definitions.

The first step of every statistical analysis you perform

1C

is determine whether the data you are dealing

1d

with is a population or a sample

1e

 a population is the collection of all items of interest to our study

and is usually denoted with an

1f

upper case N

the numbers we've obtained when using a population are called

1g

parameters.

1h

A sample is a subset of the population and is denoted with

1i

 a lowercase n and the numbers we've obtained

when working with the sample are called

1j

 statistics.

Now you know why the field we are studying is called statistics.

2

Let's say we want to perform a survey of the job prospects of the students studying in the New York University

2B

 what is the population

2C

 you can simply walk into New York University and find every student

right.

2D

Well surely that would not be the population of NYU students.

2E

The population of interest includes not only the students on campus but also the ones at

2F

Home

2G

 on exchange

2h

abroad

2i

distant education students

2j

part time students

2k

even the ones who enroll but are still at high

school.

3

Populations are hard to define and hard to observe in real life

3b

 a sample however is much easier to gather.

3c

It is less time consuming

3d

and less costly

 time and resources are the main reasons we prefer drawing

samples compared to analyzing an entire population.

4

So let's draw a sample then

4b

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

and enter the canteen because we know it will be full of people.

4c

We can then interview 50 of them.

Cool.

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

Good job

4d

 populations are hard to observe and contact.

4e

That's why statistical tests are designed to work with incomplete data.

4f

And You will almost always be working with sample data and make data driven decisions and inferences based on it.

5

Right since the statistical tests are usually based on sample data samples are key to accurate insights.

They have two defining characteristics.

5b

Randomness

5c

 and representativeness.

A sample must be both random and representative for an insight to be precise

5d

 a random sample is collected when each member of the sample is chosen from the population strictly by chance.

5e

A representative sample is a subset of the population that accurately reflects the members of the entire population.

6

Let's go back to the sample we just discussed the 50 students from the NYU canteen we walked into the university canteen

6b

and violated both conditions.

6c

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.

6d

Thus we conclude the sample was not random

but was it representative.

6e

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 have done well

OK.

7

You must be wondering

7b

 how to draw a sample that is both random and representative.

7c

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.