

def t_test(df, groups=None, groupcol='group', valuecol='value', pooling='default', samples=40000, burns=10000, thin=1, *args, **kwargs):
    "Bayesian t-Test\n\n    Given a dataframe of the form:\n\n    |Group  |Observed Value|\n    |-------|--------------|\n    |<group>|       <float>|\n    ...\n\n    Perform pairwise t-Tests on groups\n\n    Inputs:\n    dataframe -- Pandas dataframe of form above\n    groups -- (optional) list of groups to look at. Excluded looks at all groups\n    groupcol -- string for indexing dataframe column for groups\n    valuecol -- string for indexing dataframe column for values of observations\n    pooling -- strategy for using pooled data in test. \n               * 'default' -- uses pairwise pooled data\n               * 'all' -- uses pooled data from all groups\n    samples -- number of samples to use in MCMC\n    burns -- number of burns to use in MCMC\n    thin -- thinning to use in MCMC\n    progress_bar -- boolean, show progress bar of sampler (PyMC progress bar)\n\n    Returns:\n    (description, raw_data)\n    description: table describing output data\n    raw_data: dictionary of output data\n\n    "
    test = TTest(df, groups, groupcol, valuecol, pooling, samples, burns, thin, *kwargs, **kwargs)
    return test.summary()

dataset/ETHPy150Open HHammond/kcbo/kcbo/statistical_tests/t_test.py t_test/VarMisuse@67/474 `args`->`kwargs`