# Buggy code
def f_oneway(*args):
    """Performs a 1-way ANOVA.

    The one-way ANOVA tests the null hypothesis that 2 or more groups have
    the same population mean. The test is applied to samples from two or
    more groups, possibly with differing sizes.

    Parameters
    ----------
    sample1, sample2, ... : array_like, sparse matrices
        The sample measurements should be given as arguments.

    Returns
    -------
    F-value : float
        The computed F-value of the test.
    p-value : float
        The associated p-value from the F-distribution.

    Notes
    -----
    The ANOVA test has important assumptions that must be satisfied in order
    for the associated p-value to be valid.

    1. The samples are independent
    2. Each sample is from a normally distributed population
    3. The population standard deviations of the groups are all equal. This
       property is known as homoscedasticity.

    If these assumptions are not true for a given set of data, it may still be
    possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`_) although
    with some loss of power.

    The algorithm is from Heiman[2], pp.394-7.

    See ``scipy.stats.f_oneway`` that should give the same results while
    being less efficient.

    References
    ----------

    .. [1] Lowry, Richard.  "Concepts and Applications of Inferential
           Statistics". Chapter 14.
           http://faculty.vassar.edu/lowry/ch14pt1.html

    .. [2] Heiman, G.W.  Research Methods in Statistics. 2002.

    """
    n_classes = len(args)
    args = [as_float_array(a) for a in args]
    n_samples_per_class = np.array([a.shape[0] for a in args])
    n_samples = np.sum(n_samples_per_class)
    ss_alldata = sum(safe_sqr(a).sum(axis=0) for a in args)
    sums_args = [np.asarray(a.sum(axis=0)) for a in args]
    square_of_sums_alldata = sum(sums_args) ** 2
    square_of_sums_args = [s**2 for s in sums_args]
    sstot = ss_alldata - square_of_sums_alldata / float(n_samples)
    ssbn = 0.0
    for k, _ in enumerate(args):
        ssbn += square_of_sums_args[k] / n_samples_per_class[k]
    ssbn -= square_of_sums_alldata / float(n_samples)
    sswn = sstot - ssbn
    dfbn = n_classes - 1
    dfwn = n_samples - n_classes
    msb = ssbn / float(dfbn)
    msw = sswn / float(dfwn)
    constant_features_idx = np.where(msw == 0.0)[0]
    if np.nonzero(msb)[0].size != msb.size and constant_features_idx.size:
        warnings.warn("Features %s are constant." % constant_features_idx, UserWarning)
    f = msb / msw
    # flatten matrix to vector in sparse case
    f = np.asarray(f).ravel()
    prob = stats.fprob(dfbn, dfwn, f)
    return f, prob


# Traceback (AssertionError)
======================================================================
FAIL: Tests that class_weight and sample_weight are multiplicative
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/scikit-learn-0.16.0~b1+git1-gab4d07d/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/linear_model/tests/test_sgd.py", line 633, in test_weights_multiplied
assert_array_equal(clf1.coef_, clf2.coef_)
File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 718, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 16.6666666667%)
x: array([[-0.34681637, -0.32240913, -0.31335029,  0.34920789,  0.3658206 ,
0.38752489]])
y: array([[-0.34681637, -0.32240913, -0.31335029,  0.34920789,  0.3658206 ,
0.38752489]])

# Fixed code
def f_oneway(*args):
    """Performs a 1-way ANOVA.

    The one-way ANOVA tests the null hypothesis that 2 or more groups have
    the same population mean. The test is applied to samples from two or
    more groups, possibly with differing sizes.

    Parameters
    ----------
    sample1, sample2, ... : array_like, sparse matrices
        The sample measurements should be given as arguments.

    Returns
    -------
    F-value : float
        The computed F-value of the test.
    p-value : float
        The associated p-value from the F-distribution.

    Notes
    -----
    The ANOVA test has important assumptions that must be satisfied in order
    for the associated p-value to be valid.

    1. The samples are independent
    2. Each sample is from a normally distributed population
    3. The population standard deviations of the groups are all equal. This
       property is known as homoscedasticity.

    If these assumptions are not true for a given set of data, it may still be
    possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`_) although
    with some loss of power.

    The algorithm is from Heiman[2], pp.394-7.

    See ``scipy.stats.f_oneway`` that should give the same results while
    being less efficient.

    References
    ----------

    .. [1] Lowry, Richard.  "Concepts and Applications of Inferential
           Statistics". Chapter 14.
           http://faculty.vassar.edu/lowry/ch14pt1.html

    .. [2] Heiman, G.W.  Research Methods in Statistics. 2002.

    """
    n_classes = len(args)
    args = [as_float_array(a) for a in args]
    n_samples_per_class = np.array([a.shape[0] for a in args])
    n_samples = np.sum(n_samples_per_class)
    ss_alldata = sum(safe_sqr(a).sum(axis=0) for a in args)
    sums_args = [np.asarray(a.sum(axis=0)) for a in args]
    square_of_sums_alldata = sum(sums_args) ** 2
    square_of_sums_args = [s**2 for s in sums_args]
    sstot = ss_alldata - square_of_sums_alldata / float(n_samples)
    ssbn = 0.0
    for k, _ in enumerate(args):
        ssbn += square_of_sums_args[k] / n_samples_per_class[k]
    ssbn -= square_of_sums_alldata / float(n_samples)
    sswn = sstot - ssbn
    dfbn = n_classes - 1
    dfwn = n_samples - n_classes
    msb = ssbn / float(dfbn)
    msw = sswn / float(dfwn)
    constant_features_idx = np.where(msw == 0.0)[0]
    if np.nonzero(msb)[0].size != msb.size and constant_features_idx.size:
        warnings.warn("Features %s are constant." % constant_features_idx, UserWarning)
    f = msb / msw
    # flatten matrix to vector in sparse case
    f = np.asarray(f).ravel()
    prob = special.fdtrc(dfbn, dfwn, f)
    return f, prob