  We detect and quantify significant numerical biases in the determination of
the slope of power laws with Salpeter (or similar) indices from
uniformly-binned data using chi-square minimization. The biases are caused by
the correlation between the number of stars per bin and the assigned weights
and are especially important when the number of stars per bin is small. This
result implies the existence of systematic errors in the values of IMFs
calculated in this way. We propose as an alternative using variable-size bins
and dividing the stars evenly among them. Such variable-size bins yield very
small biases that are only weakly dependent on the number of stars per bin.
Furthermore, we show that they allow for the calculation of reliable IMFs with
only a small total number of stars. Therefore, they are a preferred alternative
to the standard uniform-size binning.
