I want to test the hypothesis that a group of vectors in 3D space, say given by a long list of xyz coordinates from some experiment, have no preferred direction. Is it sufficient to pick some direction in space, say the xaxis, and calculate the cosine angle between each data vector and this direction, and look at the mean cosine angle? Thanks, nuun




I presume that by "having no preferred direction" you mean that the distribution on the sphere is uniform  as it has just been pointed out by Gerry. Testing uniformity on the sphere is a classical statistical problem. There is A LOT about it: you may have a look at the book "Directional Statistics" by Mardia and Jupp (especially Chapters 9 and 10), or, for instance, at these more recent papers by Pycke and Bakshaev. 


There is a notion of uniform distribution on spheres, and a notion of discrepancy on a sphere (which is a numerical measure of the distance from uniform distribution). That should give you some search terms. One paper on the topic (probably more theoretical than you want, but it should have or at least point to the relevant definitions) is Martin Blumlinger, Slice discrepancy and irregularities of distribution on spheres, Mathematika 38 (1991) 105116. 


Here is one approach to consider. Treating the data as points on the surface of the unit sphere, consider the collection of convex subsets on this surface that contain all of your observations. Then, define $S$ to be minimum area among such sets. One way to interpret the idea of "having no preferred direction" is that this set $S$ should be almost as big as the entire surface; conversely a preferred direction would manifest as the data being tightly concentrated in a small area on the sphere. This is just a rough idea  figuring out how to operationalize "almost as big as the entire surface" would depend on your statistical needs. Hope I haven't missed the mark too badly. 

