import numpy as np


def aic_statistic(dist, data, axis):
    """
    AIC-based goodness-of-fit statistic.

    AIC = 2k - 2ln(L)

    Lower AIC indicates better fit, but since goodness_of_fit()
    treats larger statistic values as worse fit, AIC works directly.
    """

    k = len(dist.args)
    log_likelihood = np.sum(dist.logpdf(data), axis=axis)
    aic = 2 * k - 2 * log_likelihood

    return aic