BAM!
Mr. Tarrou.
Back again.
We are going to start discussing in Statistics about how you collect or create, or just get
some data to actually analyze.
We have been making, you know, pie charts, and histograms, and dot plots, and stem plots,
and you know...
Pick your graph.
We have been doing all of this analysis of data but we have never discussed yet, I have
not yet discussed how this data comes about.
How do we collect it, how do we create it through experiments.
And um, I always talk to my kids about saving money in my school as teenagers for retirement
because well...
You take that compound interest and you let it roll 30, 40, 50 years, well not 30, but
if your retirement age is 65 and you start saving when you are 16 you have almost 50
years for compound interest to roll up.
You can actually have a pretty nice retirement and not have to stress about it too much.
So I ended up for my birthday, I got a 100 dollar bill t-shirt.
So funny shirt Friday.
Anyway, that is why I am wearing this shirt.
OK, two ways of collecting data.
Two basic ways of collecting data.
There is the observational study.
You observe individuals and measurements but you are not imposing a treatment.
Key phrase right here.
So take a scenario of again talking about smoking and causing lung cancer.
You cannot take a 1000 people and randomly assign them to two groups where one group
just is forced to smoke and the other group is prevented from smoking.
And oh by the way, not only that is unethical, but you are going to have all eat the same
foods, live in the same houses, do the same job, exercise the same.
You know, every facet of your life is going to be identical except for by the way you
500 smoke and you 500 don't.
So in situations where it is impossible to run an experiment, or you just isn't necessary,
you are looking at an observational study.
Any scenario where you are not imposing a treatment.
Now what if you do impose a treatment.
Well, then you are setting up an experiment.
You impose a treatment on individuals or subjects if they are human in order to observe their
responses.
Maybe you want to find out if a new cholesterol drug helps to lower cholesterol better than
an old drug.
Ok, take some people that have high cholesterol, random assignment them to two groups...
We will talk about how to do that later.
And one group gets the old drug, the control.
Not a placebo like a sugar pill because, you know, high cholesterol could be very bad for
your health.
So maybe an old drug, and then the treatment gets a new drug.
Then you will later discuss how you compare those numbers to see if the new drug was truly
effective.
So any scenario where you are imposing a treatment would be an experiment.
And experiments are the easiest way to prove cause and effect.
Observational studies along supposedly cannot prove cause and effect.
I am sure there is some scenario where somebody will be able to argue that a tiny bit.
But, cause and effect.. and especially in life.. is very very complicated and difficult
thing to prove.
You need to have strong correlation or association.
You need to have association be consistent.
We talked about with smoking about how it did not matter what country you were in, the
more you smoked the more chance you have of getting lung cancer.
That the more explanatory variable, the stronger the response.
That cause precedes the effect.
And that it is plausible.
There are five key things to look for to show evidence for causation.
And observations along are basically not appropriate for proving cause and effect.
A well done sample will produce a group of individuals what will unbiasedly represent
the entire population, the entire group of individuals that we want information about.
So if I wanted a survey at this school about how they feel about the teachers here, well
then I could just go down to guidance, I could get a list of all the students, and randomly
just pull numbers out of a hat.
Say go randomly choose 50 students.
So the population will be all of the students at this school.
If I only wanted to survey the seniors, then same thing...
Go down and get a list of all the seniors, randomly select maybe 100 of them.. or 50..
or who knows.
Whatever you feel like is appropriate.
The population would be just the seniors of this particular high school.
So the population by definition is all of the individuals, but not like everyone in
the world, it is everybody or everything that you want to learn about or represent in your
experiment or in your observational study, or your survey.
If I do a survey of just my school about a topic, I could not therefore then.. sort of...
impose or summarize for inference that for the entire county or state.
Not if I just poll students from my own school.
So the population is the entire group of individuals we want information about.
And a sample would need to represent that particular population you are trying to learn
about.
So if I want to an opinion about just the seniors at our school, I will randomly select
just some seniors as an example.
Ok.
Moving on.
[nanananana] Alright!
Sampling involves part of a population to learn about the whole population.
Very often the best option.
Let's go back to that surveying the entire school.
Would I actually want to attempt to contact all 2400 students (I think) that come to this
school?
That would be very time consuming and unnecessary.
I could just sample about 100 and get a very good idea about what the entire school feels
about whatever I am talking about.
If I want to see what new cholesterol drug works better, or a new cholesterol drug works
better than an old cholesterol drug.
I can't run an experiment on every single person that has high cholesterol.
No, I would just go take a random sample of people with high cholesterol.
If... you know..
Think of your country or your state, probably just a county government wanting to run a
census.
How hard is it to contact everybody or everything in an entire population.
So we normally just work with sampling and then try to inference the results from the
sample to the entire population.
The definition of inference is using a sample's results to make conclusions about the entire
population.
We are not to doing that step yet.
We are just collecting numbers.
So I am going to leave that vocabulary word out for now.
A census attempts to contact every individual in a population.
These are very expensive, very time consuming, and very very often possible.
And also if you are trying to work with every subject or unit in an entire population you
will very quickly be overwhelmed by a mass of data.
That will probably lead to error that you would get from a sample.
Ok, now we are going to talk about two sampling methods.
I don't think I have an issue with time, but I am definitely going to have an issue with
space.
So this video is going to be a multipart video so that I can get all of these sampling and
experimental designs set up for you.
Today is just just sampling.
Voluntary response sampling consists of people who choose themselves to be in a general survey.
Think of radio call in surveys, tv or internet surveys.
These are most certainly negatively biased since we find it easier to complain than to
compliment.
Even about ourselves, with my students if I ask them what they like or dislike about
themselves and give them just a short period of time to come up with some answers...
maybe a minute or less even.
The list of complaints about, even about themselves, would grow very quickly especially compared
to what they are going to say they like about themselves.
You know, when we are unhappy we complain, we voice out.
And um...
Not often enough compliment people or point out what things are good.
If you are happy, you are content and satisfied and just sort of... you know... chill.
So all of these "Go to the internet and click what do you think or what you feel about...",
or "Hey, this is some kind of TV... maybe I am conservative or maybe I am liberal and
I am watching a show.
Why don't you get on the internet and give us an opinion about what you think what is
going on in politics or the economy.. or just whatever you feel like."
The radio station, "Hey, call us and tell us what you think about
so and so that happened this weekend."
All of those surveys are so common and they are almost always negatively biased.
You get something to talk about, but there is no real validity behind those results.
It is always going to be negatively biased.
There are more complaining that are unhappy about what is going on, therefore those individuals
in the population will be over represented in a Voluntary Response giving you what is
known as a Negatively biased survey.
WHOOO!
Alright:)
[nananana] Coming on the left.
WHOO!
Alright, last... not so great sampling method that we have in Introduction to Statistics.
That is Convenience Sampling.
Convenience sampling is survey those people who are easiest to reach or close by.
You know, you are not really doing in real hard work to get to these people.
It is not random selection.
You are not trying to contact them by phone or email through some randomization process.
You just...
well...
What am i talking about?
Interviewing shoppers just by going to the mall and talking to who ever walks by.
This may be ok if you want to report the habits for that mall, but it could not be applied
to all shoppers.
Let's say that you are... you do have a shopping mall.
You do have a store and you want to find out how you could improve that store.
Well if all you do is stand at your door and go, "Hey, are you happy?"
You certainly will get some negative comments and again you are trying randomly select these
people as they come in.
Or, maybe you are just...
Well, actually you are just standing at the door and they just happen to walk in.
Hey, do you like our store?
Do you like what we are doing?
Well, wouldn't the people coming to the store already be happy with what you are doing?
Um...
You might get a false conclusion that what you are doing, the way the store is set up,
the prices, whatever... that they are all fine.
But what about all those people that are not coming to your store.
Like if you find out from your shoppers if you are doing well or not doing well, certainly
it probably has some validity.
But if you really want to pull in a lot more business, why not find out what all of those
people not coming to your think.
Why aren't they coming?
Are the prices too high?
Is it not set up well?
You know, I am just giving an example of course.
But convenience sampling is just kind of chilling out.. who ever walks by.
You just survey them and see what is going on.
That is not a good sampling technique.
The convenience sampling and the voluntary response.
They take little to no work for the collector of the data, but you will not get results
that are very useful or just not useful at all.
Again, the Voluntary Response you are going to have negative bias.
With the Convenience Sampling it will be nearly impossible to get a sample that truly represents
the population that you would like to learn about.
So you do not want to do these type of sampling techniques.
We teach them because if you don't know how to do things wrong, you might not understand
what is good what is correct.
So let's get this erased, give me some time to copy a new set of notes up here.
Of course all you are going to do is hit play this video.
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