  Multifractal Detrended Fluctuation Analysis stands out as one of the most
reliable methods for unveiling multifractal properties, specially when
real-world time series are under analysis. However, little is known about how
several aspects, like artefacts during the data acquisition process, affect its
results. In this work we have numerically investigated the performance of
Multifractal Detrended Fluctuation Analysis applied to synthetic finite
uncorrelated data following a power-law distribution in the presence of
additive noise, and periodic and randomly-placed outliers. We have found that,
on one hand, spurious multifractality is observed as a result of data
finiteness, while additive noise leads to an underestimation of the exponents
$h_q$ for $q<0$ even for low noise levels. On the other hand, additive periodic
and randomly-located outliers result in a corrupted inverse multifractality
around $q=0$. Moreover, the presence of randomly-placed outliers corrupts the
entire multifractal spectrum, in a way proportional to their density. As an
application, the multifractal properties of the time intervals between
successive aircraft landings at three major European airports are investigated.
