And the idea here is that if data are randomly arrayed,

you should see just some sort of random flipping and flopping back and forth.

If you get data again that are hanging, on one side or

the other, and you only have two runs in your data,

you're gonna have not enough data that forms essentially a normal distribution.

So this table which has been figured out mathematically for years,

is designed to tell you how much variation there should be in a given set of data.

So if you had 15 data points, 20, 30,

it will tell you the lower and upper boundaries of the number of runs.

The final test, or rule, if you will, is whether or

not we have an astronomical data point.

Now, this is a judgement call,

something I refer to as the interocular test of significance.

We have data that are going along, and then all of a sudden,

wonk, we've got this huge spike, and we wonder why.

Well, often times two things.

One, we could have collected the wrong data, that for some reason,

data got into our data set that shouldn't have been there,

cuz here's where the bulk of the data typically fall.

Or, in fact something, special was going on on that day.

If this is food trays being deliver to the medical units, and

this is the day that the elevator people came and

shut down three banks of elevators, when all the food trays backed up.

And if you're looking a percent of food trays delivered on time,

you'd see this big spike.

Well, an astronomical data point is not a statistical determination on a run chart,

it's an eyeball test.

And it's guidance that you should either look at your data or

put the data on a control chart, which will be in a subsequent session to find

out if in fact that is truly different than the rest of the data.

So there you have it.

The run chart in a nutshell.

You've got the elements, x- and y-axis, the median, plot the data over time,

figure the number of runs, and then apply the four simple run chart rules.