The study of statistics is commonly divided into two categories.
We have what are known as descriptive statistics and also inferential statistics.
Lets start first by discussing descriptive statistics.
A good way of describing them is that they're really just a method of organizing, analyzing,
as well as presenting data in some useful way.
So let me give you an information on how descriptive statistics works and how it would actually
play out practically.
One of the commons themes that we see is when businesses issue financial reports.
And so financial reports are a great example of descriptive statistics because they're
merely describing something.
They're taking information, so a company's financial performance, they're organizing
it and of course they're presenting it in a way that, ideally, the reader would be able
to understand it.
And they can draw their own conclusions.
But really its just designed to present the information in its own right.
So something that might be describing a company's financial information could be showing income
from a couple periods of time.
So maybe we would have income for the past three years.
So maybe, you known, 2011, 2012, and of course 2013.
And then of course we have our individual projections on how much money was generated
in that particular period of time.
So I'm going to go ahead and fill in certain numbers here in thousandths.
This would be an example of descriptive statistics because we're merely organizing all of our
financial information.
We're obviously presenting it in a way that is somewhat useful.
And that it was much better than if it was all expressed individually.
So this is something that we would use for descriptive statistics.
You can also do such things as determining the number of people with certain characteristics
or certain traits.
That would be describing something.
The key thing to think about when looking at the difference between the two is one are
we merely describing something, or are we trying to generate assumptions based on information.
And that's really where the difference between the two lies.
Is with inferential statistics we're really trying to draw conclusions, we're trying to
make assumptions based on how a smaller group of individuals or objects behaves we're trying
to infer that the larger group would essentially behave the same way.
So in order for this to work, we need to have a smaller subset of individuals that ideally
and hopefully reflect the characteristics of a larger group.
And so some terms that you've probably heard of that are very important to inferential
statistics include is known as a sample, and also what is known as a population.
Now a sample is merely a subset.
It's a smaller group of a larger group that is designed to reflect those same characteristics.
Population is the entire group in itself.
So that the idea here is that we would survey a smaller group of people and then basically
draw conclusions and assume that well if a smaller group of people behaves this way then
probably the population will behave in a similar fashion.
So that really depends on the accuracy of our sampling right.
If we're going to select the right people because if you select the wrong people than
it's difficult to draw conclusions from.
So the way to look at this though is that the sample is really that smaller group of
the population.
And we want to try to select a sample that mirrors the characteristics if you will, like
demographics and those things, of our population so that we can draw those meaningful conclusions.
So the first thing that you're probably asking is there's going to be kind of these issues
with obtaining an accurate sample, which we'll talk more about over the course of this series,
then why don't we just continue to survey the population?
Why don't we do something like the U.S. census, which in idea is kind of a study of the population,
not everybody submits it so it's maybe more of a sample you might say.
But why don't we do something like that?
The big determinant of not doing those types of things is of course the cost of it.
And you think about if you wanted to survey a population of consumers, like american consumers,
how costly would it be to try and track all of them down to complete this survey and it
would be very very time consuming.
Not only that but very very costly from a labor as well as a explicit cost standpoint.
So that is one of the biggest reasons.
It's very hard, especially if you're a business trying to survey every one of your target
customers, to find all of them and to get them to interact with you in such a way that
they can complete the survey.
Whereas if you use inferential statistics and you simply kind of list out the traits
and the commonalities and the characteristics of the people that you want to find then you
can go out and find a good amount of those.
Enough so that you can do those conclusions and you can draw those inferences.
So that's a little bit about descriptive statistics and kind of how they compare to inferential
statistics.
We're going to talk more about those over the course of this series.
So it's important that we get kind of the purpose of both of them, but also how they
are different and how they can be used to present information in a useful way.
Thank you for watching this video on descriptive and inferential statistics.
In the next video, we're going to explore the types of variables that we commonly see
in statistics.
For questions please leave them in the comment box below and I'll do my best to get back
to them in a timely fashion.
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Thanks for watching.