def METHOD_NAME():
    rng = np.random.RandomState(42)
    p = 100
    # X1 is some effects estimate, V1 their variance for "session 1"
    X1, V1 = rng.standard_normal(size=p), np.ones(p)
    # same thing for a "session 2"
    X2, V2 = 2 * X1, 4 * V1
    # compute the fixed effects estimate, Xf, their variance Vf,
    # and the corresponding t statistic tf
    Xf, Vf, tf = _compute_fixed_effects_params([X1, X2], [V1, V2],
                                               precision_weighted=False)
    # check that the values are correct
    assert_almost_equal(Xf, 1.5 * X1)
    assert_almost_equal(Vf, 1.25 * V1)
    assert_almost_equal(tf, Xf / np.sqrt(Vf))
    # Same thing, but now there is no precision weighting
    Xw, Vw, _ = _compute_fixed_effects_params([X1, X2], [V1, V2],
                                              precision_weighted=True)
    assert_almost_equal(Xw, 1.2 * X1)
    assert_almost_equal(Vw, .8 * V1)