I appreciate that in medicine it can be. But this is not medicine. In this case the 'research' was obviously done by first letting someone cycle WITH hair, then they were shaved (with time to recover I imagine) and then they tried again. I say 'obviously' because this little participants is not enough because you'd need more people to be able to compare means in performance differences.
The problem I see however is that after being shaved the participant would feel faster and thus behave faster. A sort of placebo effect if you will. At the same time, the cyclist-cultural effect of being 'unshaved' in the first test would make them feel sluggish. The same effect as seen in experiments where participants are presented with negative messages vs positive messages and asked to perform tasks. The participants exposed to negative messages, as expected, perform poorly in comparison to the other group.
In conclusion, I feel, that this 'research' can only be used as something to base you hypothesis on, but it is in no way conclusive in its current setup. You'd need to have random groups of cyclists, some of whom are, to begin with, shaved, and some who are not. It would maybe be easier not to use cyclists in order to find a diverse enough set of participants. Then again I don't know how many regular cyclists go around unshaven.
This wasn't "how fast can you get from A to B" - they were cycling at a steady rate on a stationary bike in a wind tunnel. Pedalling faster would not trivially make the results better (it might have some complicated aerodynamic effect, but I don't have any clue of the likely size or direction).
The number of people needed to archieve statistically significant results more or less solely relies on the size of the effect that gets measured[1]. It's actually not unusual to design experimental studies with just a single person - provided the "treatment" (shaven legs in this case) are reversible. Then you just have to make repeated phases with and without treatment , e.g. ABABAB or even ABACABAC if you have an alternative treatment you want to compare the results with.
[1] If you design a study you can actually start from the other end: If you can estimate the size of the effect to be measured, you can calculate the number of people needed for significance beforehand.