def askewtest(a,dimension=None):
    if dimension == None:
        a = N.ravel(a)
        dimension = 0
    b2 = askew(a,dimension)
    n = float(a.shape[dimension])
    y = b2 * N.sqrt(((n+1)*(n+3)) / (6.0*(n-2)) )
    beta2 = ( 3.0*(n*n+27*n-70)*(n+1)*(n+3) ) / ( (n-2.0)*(n+5)*(n+7)*(n+9) )
    W2 = -1 + N.sqrt(2*(beta2-1))
    delta = 1/N.sqrt(N.log(N.sqrt(W2)))
    alpha = N.sqrt(2/(W2-1))
    y = N.where(y==0,1,y)
    Z = delta*N.log(y/alpha + N.sqrt((y/alpha)**2+1))
    return Z, (1.0-zprob(Z))*2
Tests whether the skew is significantly different from a normal
dataset/ETHPy150Open radlab/sparrow/src/main/python/third_party/stats.py/askewtest