Hello!
Mr. Tarrou.
You might want to pause and copy this page of notes.
Ok.
So we just got done talking about the difference between observational studies and experiments.
We are going to start collecting data instead of just being given all of this information
to make graphs and pie charts, histograms, and so on and so on.
We talked about two bad ways of collecting data.
Those would be Voluntary Response setting where you just pose a question to the entire
population and let whoever wants to respond respond.
Like on TV or on the internet, or call in surveys.
We also talked about Convenience Sampling which is also not a good way to collect data
through a sample.
We then did some proper techniques like Simple Random Sample, Stratified Random Sample, Cluster
Sampling, and Multistage Sampling.
Well now I am going to give you some cautions about Sample Surveys that we are attempting
to do to collect data.
This idea of Undercoverage which occurs when some groups in the population are left out
of the process of choosing a sample.
Now days where I live a lot of people are starting to abandon their land lines, the
hard land lines, for the use of cell phones exclusively.
So if I want to do a phone survey, and all I do is pick a phonebook and start calling
phone numbers then I am under-coveraging the people that only use cell phones.
Now if the people that you cell phones are the same as the people that use just land
lines then that really may not cause a problem with my survey.
But, if there is a difference between those populations, if people that tend to use cell
phones tend to be younger and more mobile than those that use exclusively land lines
or both cell phones and land lines, then that under-coverage may cause my sample to be biased.
That is because I am leaving out a part of the population that would give a different
response to my question.
If that is pertinent.
Because you know, we are trying to learn about a part of a population with our sample.
If that difference is not important, then it won't be an issue.
But if there is a difference, if it is important to your survey then that could be under-coverage
that would be a problem.
Non-Response when individuals chosen for the sample can't be contacted or refuse to cooperate.
Though we voluntarily choose to take part in a survey, this is not the same as Voluntary
Response because we are contacted individually.
No one can force us to answer questions in a survey.
No one can force us to be in an experiment, not that we are discussing experiments at
the moment.
So there is always this idea of voluntarily being a part of whatever this survey or sample
is if you are contacted of course to be in a survey yourself.
Again, this is not the same as Voluntary Response.
Remember Voluntary Response setting is the news paper saying write in, or write your
congressman...
Or the TV personality saying get online and vote in our online poll to give us your opinions.
They just blanket the entire population with one request for responses.
That is Voluntary Response.
We find it much easier to complain unfortunately than it is to compliment.
If we are happy and content we don't make a lot of noise.
So Voluntary Response settings tend to be negatively biased.
I talked about that in the last video.
Non-Response is you get contacted individually and for some reason you refuse.
If you can identify a part of the population, you as the statistician, if you can identify
part of the population that has a much higher non-response rate you can resample just that
part of the population to try and fill in the gap.
If you are doing a sample survey and you notice that large businesses tend to return your
requests or answer your questions less often than small businesses, then you could do a
small SRS of just large businesses to try to fill in the gap in your data from all of
the non-response.
If you are sampling people in different regions like rural areas in the country, suburban
areas, or big cities... if you want a sample that represents all three of those groups
and you notice that a lot of people in the city saying "Go buzz off", then that high
non-response in just the city area would cause an issue with your sample and possibly cause
it to be biased and not represent the population.
So you could do a small SRS of just the large city people to try and pull in some more of
those responses to fill in the gap.
So there are ways to sometimes deal with non-response.
Then other times, you know, there is always going to be non-response in every sample survey.
It is just something that we have to deal with.
And... you know...
It can never be removed completely.
Undercoverage, you leave people out.
Nonresponse, those people are choosing to not respond.
It is always going to happen.
But, if there is an area of your population, an area of your sample, that has a much much
higher non-response rate you can.. you are allowed to go back and do a small SRS to try
and fill in that gap with just part of the population.
WHOO!!!
[nanananana] Ok.
I talked about this in my last video a little bit.
But you can have bias in a survey from a poorly designed survey.
If I want to see what percent of people at home bake and I just call people at home during
the daytime hours, maybe I get a higher sample proportion of people saying they bake as opposed
to if I called in the morning, in the afternoon, and late evening.
Possibly catching more people that work in the early morning and the late evenings.
So I can have biased response in response, in my survey, in my sample from a poorly designed
sample.
I can have bias from the behavior of the interviewer, an embarrassment about the true answer so
they lie, race or sex of the interviewer, or faulty memory about long past events.
If somebody comes to my door that is a minority and starts asking me racially sensitive questions,
I might be embarrassed or not want to answer honestly about those questions.
If some nuns and priests come through the neighborhood and they are finding out, they
are doing some kind of survey about their church and religion, I might be a little bit
more pressured to say something positive about religion because these people are to be respected
because they are priests or nuns.
If you come to me and you ask about whether I do drugs or drink a lot?
Those might not be questions that I want to answer someone face to face because I would
be embarrassed and lie.
And then underestimate or... you know...
Say, "No I don't drink."
or "No, I don't any drugs."
You know if you embarrass somebody you are not going to get an honest answer out them
face to face unless something is set up to sort of allow them to answer anonymously about
questions that may be embarrassing.
You can have Wording Bias.
When the question included leading information making one response more likely.
Again if I said some expert says blah blah blah, how do you feel about it?
You might be more inclined to agree with the expert's opinion that I gave you before I
started asking the question.
So your wording bias can be an issue and give you faulty responses.
There...
I don't remember if it was in my book or something that I read on line, that there was this scenario
where they were testing whether or not people preferred Coke or Pepsi.
Well, that is kind of not really a sample survey.
Where they said here is cup A, here is cup B, taste them and tell us which one you prefer.
Well, they did not randomize what order people drank the sodas.
So it became biased simply because they more often chose the last taste they had in their
mouth, or something like that.
Where they did not randomize the order in which the soda was drank so the results came
out to be biased.
That was biased that was not from Response Bias, it was not from Wording of the Question.
It was simply not randomizing the process of the sample (tasting).
And then we have this idea of inference.
This is a huge concept.
Inferencing is what Statistics is all about.
We take small samples, we analyze the sample, we draw conclusions, and then we..
sort of... inference or make a conclusion about the entire population based on what
we saw in the sample.
So an inference of the population is making conclusions about the population from a sample.
These samples are only estimates and have variations in their results.
If 40% of the people in a local area shop at a certain grocery store, if I go ask 100
people, "Hey, do you shop at store x?"
You are not going to get exactly 40 percent.
You are going to get around 40 percent because you are only sampling a small portion of the
population.
And not quite a survey, but even if you are just flipping a coin you are going to probably
not get the same results twice.
There is variability which makes sampling distributions whose mean is sample mean or
proportion, like when you flip a coin.
Let's talk about flipping a coin for a second.
You know if the coin is fair, it is perfectly balanced you are going to get heads in the
long run 50% of the time.
Well if you flip a coin twice is not unlikely that you would get heads two times in a row
or a 100% of the time.
That is certainly 50-50 even if you flip a coin 50 times you are not going to get an
exactly 25 heads and 25 tails.
You are going to get around 50% heads or tails.
That variability is what makes sampling distributions something we will talk a lot more about later.
So the mean of your sample or the mean proportion, this would be like you flipping a coin 20
times and recording the percent of times you got heads, is if unbiased be equal to the
population mean....
Sample mean.. population mean.
Sample proportion..
Population proportion...
if the sample is unbiased.
So that means is if 40% of people in a certain local area shop at a store and you survey
100 people you are not going to get exactly 40%.
You could, but it is unlikely.
However, if you repeated that sampling which is not necessarily a good idea in statistics.
But as far as the idea of sampling distributions go, if you were to continually sample unbiasedly
100 people in that area over and over and over and over again, and then take your results
and make a histogram out of it, it will be centered around the .4.
40% of the population, I should be pointing to the p.
If 40% of the population shop at that store, then your sample results will center around
.4 if it is unbiased.
Ok.
You flip a coin.
If you flip a coin 20 times you are not going to get heads 50% every single time.
But if you flip that coin 20 times, report the percent of head, flip the coin 20 times,
record the percent of head, flip the coin 20 times, record the percent of heads, and
so on.
Repeat that process many many many many times.
And then make a histogram out of all those proportions that you observed out of the 20
flips, that sampling distribution will center around the true .5 if the coin is indeed fair.
Ok.
One more screen and we are done for this morning.
WHOOO!
[nananana] Alright, last two statements.
You are going to learn a lot more about these two statements as we go through the year.
But, as an introductory statement..
Larger samples have less variability, and thus are more accurate.
But is possible for a survey or an experiment to actually be too accurate.
If you are doing quality control at the end of an assembly line, and you do such a large
sample that....
Let's sawyer are filling some bottles.
If you take such a large sample that you can tell that the soda bottles are being filled...
let's say by... if it is off by more than half an ounce.
And you stop the lines and go, "WHOOOO...
alright... something is wrong with the machine!"
We are under or over filling the bottles by half an ounce, stop the presses and make some
adjustments.
Well the boss says, I can only make adjustments on one once increments.
Then you have made a test that is too sensitive.
You are telling them that it is off by half an ounce, but they cannot...
It has no real life applications.
Adjustments cannot be made that small.
I am sure they probably could be.
But the point is you can reduce the variability to such a degree, that you get statistically
significant results...
Not that you know quite what that means yet...
But you can, you know, put your hands up and yell something is weird... something is changing...
and yet it has no real life application.
It is too sensitive.
And as long as the population is at least ten times greater than the sample size, you
will be using that phrase a lot this year, it is only the size of the sample that determines
the variability of that sample.
So I am recording these videos in Florida.
If I take a sample of 500 voters, and say in Florida...
Make a conclusion.
If I take a sample of 500 people in the entire United States and draw a conclusion.
Those two samples have the same variability because they both have a sample size of 500,
even though the state of Florida has a population that is much much smaller than the entire
US.
So, if the populations are greater than ten times the sample size, it is only the size
of the sample that determines the variability of the sample.
You will notice that when you look at your standard deviation formulas.
For all the different types of variables that we ar going to learn, but that is a much later
and a much longer discussion.
WHOOO!
Mr. Tarrou.
Thank you for watching.
I am hoping they are helping.
Go Do Your Homework and read your book.