In this lecture, we're going to discuss a contemporary application of the simple moving average, since
this is still fresh in our minds, I think it's a great opportunity to see how basic concepts like these
get applied in the real world.
So covid-19, as you know, many data sources use a seven day rolling average to report the trend of
new accounts.
Using what you've learned in this section, you have a kind of insider view of the pros and cons of
this approach.
On one hand, this is very useful.
We've now seen real world reasons for statistical noise.
For example, sometimes there are data entry errors that needs to be corrected later.
This will skew the case counts.
Maybe they'll be undercounted one day and then overcounted another day to correct for the previous undercounting.
Another example is where a specific locations don't always report their accounts promptly to whatever
central authority is providing their accounts to the public.
In this case, again, this will skew the case counts.
The counts might be undercounted one day because some locations forgot to send their counts and then
overcounted the next day when the backlogged counts are included.
We've already seen how accounts from two different sources can disagree.
You'd think a simple task like counting would be error free.
But these are the complexities of the real world.
We've also seen situations where incompetent politicians direct people to simply lie.
In this case, again, accounts are inaccurate.
Now, simple moving averages will not help you in this situation, but they do help you when the counts
are simply delayed or backlogged.
So using what you've learned in this course, what can we say about these seven day rolling averages?
Well, we know that while it can smooth out statistical noise, it also has a lag.
So if you see a very high seven day rolling average, this means that, yes, things are bad.
But they were already bad a few days ago.
In other words, it's old news.
You might think an exponential moving average might help.
But remember, this data needs to be communicated to the public.
Most members of the public do not have the capacity to understand what an exponential moving average
even is.
Furthermore, because it involves a tunable parameter, specifically the decay rate, it's not clear
how you could be consistent with everyone else in the world so that the counts are more easily comparable.
So I hope this lecture helped you understand one real world application of the simple moving average
as it pertains to science reporting, you've seen why such a technique is useful, but you've also seen
the ways in which it is not useful.
You now understand some of the real world issues that data scientists face when trying to transform
data in such a way that can be easily communicated to others.