Imagine two buttons. One is large and close to your cursor; the other is tiny and on the opposite side of the screen. Even if you notice both instantly, the tiny distant button usually takes longer to select accurately.
Fitts’ law formalizes that speed–accuracy relationship.
A widely used formulation is MT = a + b × log₂(D/W + 1), where MT is movement time, D is distance to the target and W is the effective target width. The constants a and b are fitted to a person/device/task.
The logarithmic term is called an index of difficulty. You do not need to calculate it to benefit from the idea: smaller and farther targets are more demanding.
If one aim drill uses huge targets close together and another uses tiny targets spread across the screen, their times are not directly comparable. The second drill intentionally creates more difficult movements.
Progress is easier to interpret when target size and layout are controlled or recorded.
Fitts-style models also describe touchscreen pointing, although fingers introduce additional issues such as occlusion and the size of the contact area. Small touch targets can become difficult even when the movement distance is short.
This is one reason mobile and mouse Aim scores should usually be tracked as separate baselines.
It primarily models pointing/movement time. A full target-acquisition score also includes detection and planning.
D is the movement distance and W is the effective target width along the movement direction.
They leave less room for endpoint error, so you must control deceleration and final pointer position more precisely.