It is often possible to observe large section of data concentrated in a narrow area, which is distant from the median, especially in skewed data stacks. Existing histogram binning methods pay little attention to this situation. Also, current histogram bin calculation approaches like Scott's normal reference rule [Scott (1979)] or Freedman - Diaconis rule [Freedman and Diaconis (1981)] , contain parameters like \hat{s} and/or n , which cause to generate variant histogram peaks due to skewness and/or sample size of data stacks. This study attempts to suggest an alternative ad-hoc approach, which aims to generate robust histogram peaks; irrespective of sample size, skewness or standard deviation of the data stack.

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