scipy fit for t distribution seems broken for bi-modal data I am using the scipy.stats.t.fit function, and I am surprised by the results. If I fit on some bimodal data, say

data=[1,1,1,1,5,5]


I get 

df=0.39126249808550329
loc=1.0
scale=5.7172845190830792e-21


That is, the scale is effectively zero, and I will never be able to sample anything near 5, just the more frequent data point 1. 

I guess you really can't fit on data that is too different from a t-distribution - but is scipy really giving the best t dist fit to the data? I would think that if I compute a sample mean and variance myself, i.e, 

df = 5
loc = 2.33
scale = 1.88


That I'd have a better fit, although I haven't computed the likelihood of sampling [1,1,1,5,5] from these two t distributinos.
The t-test has many assumptions. That dataset violates several of them:


Data should be sufficiently large (>30 independent points)
Data should be approximately normally distributed


Given that the assumptions are violated, you can not expect to valid results.