  In robust statistics, the breakdown point of an estimator is the percentage
of outliers with which an estimator still generates reliable estimation. The
upper bound of breakdown point is 50%, which means it is not possible to
generate reliable estimation with more than half outliers.
  In this paper, it is shown that for majority of experiences, when the
outliers exceed 50%, but if they are distributed randomly enough, it is still
possible to generate a reliable estimation from minority good observations. The
phenomenal of that the breakdown point is larger than 50% is named as super
robustness. And, in this paper, a robust estimator is called strict robust if
it generates a perfect estimation when all the good observations are perfect.
  More specifically, the super robustness of the maximum likelihood estimator
of the exponential power distribution, or L^p estimation, where p<1, is
investigated. This paper starts with proving that L^p (p<1) is a strict robust
location estimator. Further, it is proved that L^p (p < 1)has the property of
strict super-robustness on translation, rotation, scaling transformation and
robustness on Euclidean transform.
