particularly small targets, making things bigger or smaller, right?

And expanding the size of objects.

You're dragging things around.

You're clicking on things.

It always feels a lot more frustrating for me to do that with a trackpad than it does

with a mouse, and Fitts' Law here, can help us explain why, right?

Everything actually just takes longer.

All right, so before we finish up our discussion about Fitts' Law,

I want to cover a few more pretty interesting points about Fitts' Law.

The first is, in the relation to Fitts' Law and accuracy.

Now when I taught this course last semester,

I taught our local user interface design course last semester.

My students loved Fitts' Law.

It's this really nice structured explanation of something that I think we

all have sort of struggled with and maybe implicitly wondered about over time.

But everyone was very confused as to why it just spoke to speed

rather than accuracy, right?

Pointing speed is important.

How fast can you click on things?

But also important is how often will you not click on the correct button, right?

I think we've all had that experience quite a bit.

Now Fitts' Law does indeed only directly speak to time but

it does imply something of a speed, accuracy trade-off.

And the way that this works is we have to think about size in a different way.

Let's think about a target size as a center-point and a threshold.

So what Fitts' law says is that if you increase your error threshold,

if you increase your target size, your time will go down.

W is in the denominator of the equation.

But if you decrease your target size, you lower your air threshold,

decrease your target size you're going to need more time.

So there's a speed Accuracy tradeoff.

So, that's sort of at a high-level of how Fitts's Law speaks to accuracy.

I will say, I did some additional research into this, because students had so

many questions and it turns out that there was a paper published at ACM SIGCHI,

which is the same publication venue as the Google paper was published that we

talked about earlier.

This is a paper by some of our colleagues at the University of Washington,

Microsoft and York University in Canada.

And what they found was that yes, Fitts's Law actually can be manipulated

to very specifically predict the accuracy of a pointing task.

The prediction accuracy was actually somewhat astonishing.

It was in the R-squared for those of you who understand what that means

was well above 0.9 and this is a nice little excerpt from their paper.

What you see here on the x-axis is the percent of the movement time or

the time to complete a pointing task,

that it took a participant to do it without any time pressure.

So here, it's 40% of the no time pressure pointing task.

And here, it's 140%.

And what this basically says here is if you rush people,

you're going to get pretty low accuracy.

So for instance, if with very small targets,

with 16 pixel targets, the error rate was 0.8 or

80% and the accuracy was 20% if you really rush people.

So Fitts's Law, they did this through manipulation of Fitts's Law and

it turns out that Fitts's Law actually can help us understand how many times you're

going to miss-click on something.

Basically, if you make your target size pretty small and people are in a rush,

doing things quickly, you're going to have a lot of miss-clicks.

The second thing I wanted to add about Fitts's Law is that is not only applies

to sort of older input devices and sort of things like mice and

track pads and so on and so forth, but it is also helping us do important

research on new types of input devices and developing new user interfaces.