TTest did something weird when connecting with empirical distribution

[julianje]

Test = Bunny.DataTest(Bunny.TestLibrary.TTest())

Experiment = Bunny.Experiment([Effort,NoEffort],Test,"Baby effort experiment")

Bunny.Explore(Experiment) /Users/julianjara-ettinger/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:83: RuntimeWarning: Degrees of freedom <= 0 for slice warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning) /Users/julianjara-ettinger/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:117: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) /Users/julianjara-ettinger/anaconda/lib/python2.7/site-packages/scipy/stats/stats.py:3107: RuntimeWarning: invalid value encountered in absolute prob = distributions.t.sf(np.abs(t), df) * 2 # use np.abs to get upper tail Exploring sample sizes ... No limit specified. Testing samples between 15 and 35 ... Estimating time .... Traceback (most recent call last): File "", line 1, in File "Bunny/BunnyFunctions.py", line 14, in Explore res = ExploreSampleSize(Exp) File "Bunny/BunnyFunctions.py", line 148, in ExploreSampleSize Power.append(Exp.GetPower(samples)) File "Bunny/Experiment.py", line 55, in GetPower return sum(results)*1.0/len(results) TypeError: unsupported operand type(s) for +: 'int' and 'list'

[julianje]

Fixed errors in DataTest library