00:01
Okay, so in our first question, we're asked, what isn't a characteristic of graphical excellence? and this isn't so much a definition -based question, it's more of them indirectly asking you something else, okay? what they're trying to ask you here is just, what makes it good graph good? so when you make a graph, why are you making this graph, right? like, what are you trying to achieve by making this graph for someone? so let's look at a.
00:28
The ideas and concepts that the statistician wants to deliver are clearly understood by the view of the graph.
00:34
And this is something that we're definitely trying to do when we make a graph.
00:37
We aren't trying to make things more difficult to understand.
00:40
We're trying to make them simple to understand.
00:42
So in this case, it might be the relationship between a couple of variables, right? so two variables.
00:47
That's what we want you to understand.
00:49
We don't want you to get boggled down with all the nuances.
00:53
That's our job.
00:55
Okay, our job is to take what seems like a lot and make it in a more digestible format for people to see and learn from.
01:07
This is definitely what we're trying to do so we can eliminate it.
01:10
Now, b, the graph encourages the viewer to compare two more variables, to depict relationships between the variables, or to explain how and why the observed results occurred.
01:22
Yeah, i mean, isn't that what we're trying to do? we're trying to compare to variables to show a certain relationship, or maybe try to explain how these things occurred, how the things in our graph occurred.
01:36
This is exactly what we're trying to do and we make a graph.
01:40
And in fact, that's kind of the entire point of us even doing the experiment to get the data to make that graph.
01:48
So yeah, this is definitely true.
01:51
See, there's no distortion of the data that we were, yeah, there's no distortion of what the data reveals.
01:59
And that's right too.
02:01
Why would we be trying to distort what the data reveals? we're trying to make it more we're trying to make it digestible and accurate, right? we're not trying to make it harder or faker for anyone.
02:16
Now, d, the graph presents small datasets concisely and coherently, while large data sets are easily presented in tabular form.
02:25
This is where we have to put the brakes on and slow down and read this, because the first half sounds like fine, right? small data sets are represented concisely and coherently.
02:36
That's fine.
02:37
But large data sets and tabbytes.
02:39
Form.
02:40
Does that mean we're going to be giving people 60 pages of raw data for them to be pouring through? that's exactly the opposite of what we're trying to do when we create these graphs.
02:50
Like we're trying to make it more, we're trying to make it simple.
02:54
Giving someone a 60 -page document full of a bunch of numbers is not making it simple for them.
03:01
So this is a red flag...