And of course I want to scale an axis, but this line would represent my 0.307.

My median line would represent my 0.313.

My Q3 line would represent my 0.321.

And then I have my max and my min here.

And that's how we create our box plot.

Now, imagine if I had another group, if this was American League,

I can draw another group next to it.

And this allows me to directly compare data distributions between

different groups.

So, for example, if I have group A and group B,

I can draw their box plots next to each other, I can see that group B has a much,

much lower, let's say, batting average than group A.

But we can see that group A has a much,

much longer skew in the distribution here,

where the distance from Q3 to Q2 is smaller than Q2 to Q1, but

much, much closer in proportion than in group A.

The other additions we can make is,

instead of having the whiskers be max and min, is instead,

sometimes the whiskers are equal to 2 times the IQR.

And so the whisker may wind up being here and here.

And even though there's samples outside of those,

we'd then draw the samples with little stars, and those stars may be outliers.

So the whiskers can be defined based on [COUGH] some method of

outlier detection, automation.

And so, again, we can take our knowledge of quantiles and transfer this to a visual

representation, the most common of which is the box-and-whisker plot.

It allows us to quickly see things like the median, the interquartile range,

potential outliers, and

allows us to even compare statistical distributions between different data sets.