Hello, guys, and welcome back to another class of our course, I want the complete introduction to
data science and python programming.
So in this class, we are going to have a basic introduction to some statistical concepts showing the
best class.
We talked about what our statistics and in this class, we are going to talk about some basic statistical
concepts as we see the statistic.
Concepts that we are going to cover in this class would be the sampling.
And this is exactly what we are going to talk about.
So let's jump right into it right now.
So basically, it's possible.
Well, in statistics or in the world in general, there is a population.
So let's say, for example, we are studying, we want to study a certain population, want to study,
for example, the population of the United States.
So to know, for example, if they love pizza, once again, it's just an example.
So we can ask each citizen of the US where each person who lives in the US, if you love pizza by sending
them a survey, by just asking them a question or whatever.
So basically you can ask the whole population, but it's not possible to ask the whole population because
first of all, it's going to take too much resources to monitor control and will be able to ask the
whole population this question if they like pizza, for example, and also because logistically it's
almost impossible and there is no well, there will be no outcome.
So basically, you will not make more money by asking the whole population if they love pizza.
So what exactly would we do?
So instead of asking the whole population to ask a sample of the population and this is where we have
the difference between a population and a sample.
So basically, as you can see here, the population will be the whole well, all the US citizen, all
the US population, and the sample will simply be a part of this population to who will ask the question.
So first of all, having a sample is way easier.
So in this case, when you guys have a sample to who you will ask a certain question.
It's easier to monitor, it's easier to control, it's easier to find a sample than to ask the whole
population.
Logistically, it's way easier.
And usually the results are pretty much good.
But there is a lot of different sampling techniques and then not all of them are well are perfect,
but usually they work pretty well.
So you have two types of sampling methods.
So they have probability sampling method and no probability sampling method and basically the main difference
between both of them.
So probability and no probability is that the probability sampling method will be based on selecting
random people.
So you will try to select the much random people as possible and then no probability sampling method
will not necessarily select random people always.
So yes, we'll have random people, but you will understand the next few slides why it's not as well
as random randomized as the sample sampling, the probability sampling method, which is based really
on random people.
Does it make more doesn't make the probability sampling method worse than the probability sampling method
because the people are not necessarily 100 percent random?
No, but at the same time it's not the best.
So usually experts prefer using probability sampling method because it's based more on random people
and it's really well, it's more randomized than the non the non probability sampling method.
But once again, sometimes it's way more practical to work with non probability sampling method, even
if it's not the best then to use the probability sampling method.
So let's understand what is exactly the same thing method and what are all the the the sampling methods
right here.
So basically the probability sampling method we have for sampling methods.
So we have the sample random samples, the systematic samples, the stratified samples and finally the
cluster sampling.
The sample, a random sample is basically, let's say, for example, we have a population and of one
thousand people, for example.
And you want to select, let's say, ten people, you need ten people for your sample.
So instead of saying, let's say, I don't know, isolate selected, I will select really random people
of those one thousand persons.
So I will select for example.
I will.
Really, ten random people, and this would be really simple, random sample, and this is really simple
to do, so you just select 10 persons.
The next one would be the systematic sample.
It's pretty much the same.
Well, it's not the same thing as the simple random sample.
It looks like the simple random sample, but it's a bit more different.
So let's say, for example, we have once again our population of one thousand people, and that will
give we'll give a number to each person.
So let's say, for example, each person will be number one, number two, number three until one thousand.
Then from that moment, let's say, for example, you select the person number five.
And from this person, you say, OK, each plus, then I would be used inside of my sample.
So in other words, you select person number five, then the next person in your sample would be the
person number 15 to take the person number five.
Fifteen twenty five.
Thirty five, forty five.
So as you can see in the picture here, you will select the person number five, your person.
Twenty five, fifteen, twenty five is going to be the exact same jump each and every time for the stratified.
So the next.
So this is for this systematic sample.
As you can see, it's not really complicated.
So the next one would be the stratified sampling.
This one is a bit different.
So once again, it's based on the random people.
But you want to let's say, for example, you want to to have a clear how you said you want to have
a clear representation of each population inside of your sample.
So let's say, for example, you have once again your population of one thousand people and you have
two hundred people who wear a green t shirt and 800 people who wear, let's say, a blue t shirt.
So what you will do, you will select two and you need a sample of 10 persons.
So you will select two persons who wear a green t shirt and you select eight persons, wear a blue t
shirt just to be able to represent properly each population inside of your sample.
So you will not select just random people in this population, because let's say you have five persons
who wear a green t shirt and five persons who wear a blue T-shirt, that this will not be representative
of your population because you have way more persons with a with a blue t shirt.
So in this case, let's say you select eight persons with a blue t shirt, but you will select random
people inside of this population of eight hundred people with a blue t shirt.
So basically the stratified sample is really selecting is really selecting people well, the right amount
of people in each group.
So basically, if you have a bigger group of people with a blue t shirt necessarily, you will select
a well, a higher proportion of people in the in the group of people who have blue t shirts.
So this is for the stratified sample.
The last type of sample that is in the probability sampling method would be the cluster.
The cluster sampling and basically this method is really well involves dividing the population into
subgroups and that each subgroup would have similar characteristics, characteristics so to the whole
sample.
So in other words, instead of sampling individual individuals from each subgroup, you randomly select
entire subgroups.
So basically, what does this mean?
It means you will work with that.
Well, small you will.
Let's say you have a population of one thousand people once again.
And this population, you will divide it into groups of, let's say, five people.
So instead of selecting one person, you will select a whole group of five people.
So it's like the random sampling, the sample, random sample.
But instead of working with one person, you will work with small groups.
So basically this would be groups.
So this is for the probability sampling methods.
As I said, those methods are really into random.
So you work with random people and as those are really the most used by experts because they are more
representative of the population and you have more representative numbers in general with those sampling
methods.
So the second type of sampling methods will be the non probability sampling method.
And that in this case, as I said, this one is not well, it's a bit different from the sampling method
because you will work with people not who you know, but that are more accessible to you.
So basically, you will select the people with who who you ask or who you will conduct the survey or
whatever you want to do.
So you study.
Let's say you want to study people who love pizza so you will decide to who you ask.
The question, so let's see those for sampling method, so first of all, we have the convenience sample,
which is the first one right here, then we have the involuntary response sample, the purposive sample,
and finally the snowball sample.
So first of all, we have the communion sample.
This one is, well, the simplest basically how it works.
It's pretty simple.
It's imagine you and you need a sample of 10 people and you don't select random people.
You just select people who you know.
So in this case, let's say, for example, you have friends and this is way more convenient for you
to ask your friends to fill up the survey than to go and ask some strangers somewhere.
So basically, this is the convenience sample.
So you ask, let's say, for example, your friends, you tell your friends, you have your sample of
10 people and here we go.
You have well, you have your sample and you conduct your study just with your 10 friends.
But let's say, for example, the ten of your friends, like pizza once again, is not representative
of the whole population because once again, you controlled the sample.
So this is why it's not always a good thing.
The second thing would be the voluntary response sample.
This one is pretty much as the convenience sample.
But instead of asking your friends, you ask, well.
You will ask people to answer, well, with surveys and everything, you ask people to volunteer to
respond to your sample.
So basically you let's say, come somewhere, you drop your survey and people come and fill up your
survey.
Once again, this is really limited and this is not really random and representative of the whole population,
because let's say, for example, you're at the university, you come at your campus somewhere, you
sit down, you put on your survey, you will not necessarily have access to the whole population or
to a representative sample.
You will have people, let's say, from one department who are studying at that department, at that
university.
Who will Majali answer your survey, which is not really representative of the whole population.
It's just representative of the department where you conducted the survey in question.
All right.
So this is for the voluntary response sample and then after that will have the purpose of sampling.
So this method of sampling will simply involve the researcher using their judgment, judgment to select
a sample that is the most useful to the purpose of the research.
In other words, you select the people to who you will to you will conduct the research.
So once again, this is not representative at all, in my opinion.
Once again, of in this case, if you want to if you want to stay as random as possible, this is not
representative at all.
Why?
Because once again, the researcher will select the people who for him are fit to conduct the research.
So let's say, for example, you want to look for people who, I don't know, luff be used and the researcher
wants to have a positive, let's say, for example, a positive positive answers in his in his, I don't
know, his survey.
So he starts asking people who he knows love pizza to answer his service.
Once again, the results will not will not necessarily represent the population in general.
So basically purpose of sampling is just that.
Well, it could work in some cases in cases that you really need some exact people, some really you
need subjects that are that fit, that fits certain criteria.
But if you want to stay as random as possible, it's not the best way to conduct the research.
This is the third type of non probability sampling method, and the last one would be the snowball sampling.
So in this case, you will do the exact same thing.
So it's going to be the way it works.
It's like convenience sampling, but just with a snowball effect.
So I'd say, for example, you ask two of your friends to answer your survey and you ask your friends
to share your survey with some other people so your friends will share your survey with people who will
share your survey with other people, etc..
So basically, as I said, it's like a convenience sample, but just with a snowball effect, in some
cases, this could be really effective.
So let's say, for example, you are studying a I don't know, you are studying a certain community
somewhere in the country and, you know, just one person inside of this community.
So maybe this person, you can ask the person to fill up your survey and ask this person to share,
for example, this survey in his or her community.
And in this case, you will be able to conduct your study on this community in general.
Once again, this is not necessarily representative of well, of the community because it's maybe representative
of the community in a certain place.
So, for example, you are studying, I don't know, you are studying population of people who wear
a blue t shirt in the United States.
But you are conducting your research, let's say, in New York City.
So you ask someone to fill up your survey and to ask.
And since you don't know people who are wearing blue t shirts, so this person who is wearing a blue
t shirt will simply hand your survey to other people who are wearing blue t shirts.
Once again, this is just an example for you guys to understand.
So in this case, it could be representative.
But until a certain point, and it's not like the best way to represent well, to to represent and conduct
a random study to represent the population in general and to conduct a study.
So this is for these sampling methods that exist.
So, as I said, you have eight of those sampling methods.
You have two groups of sampling methods.
You have the probability sampling method and the non probability sensing method to understand the difference
between both.
The first one is really randomized.
So you ask some random people and you work really with random people, which is more representative
of the population in general.
And the second one would be the non probability sampling method, which is not.
Necessarily worst or which is not necessarily.
Well, it's more efficient and easier to do because you will ask, for example, friends.
You ask people who you know, once again, it's not necessarily representative of the whole population.
It could work.
It could bring you numbers.
Once again, those numbers are not necessarily exact.
So that's it for this part of the course.
Guys, right now, you know the difference between all the sampling methods and all in our next class.