  We suggest partial logarithmic binning as the method of choice for uncovering
the nature of many distributions encountered in information science (IS).
Logarithmic binning retrieves information and trends "not visible" in noisy
power-law tails. We also argue that obtaining the exponent from logarithmically
binned data using a simple least square method is in some cases warranted in
addition to methods such as the maximum likelihood. We also show why often used
cumulative distributions can make it difficult to distinguish noise from
genuine features, and make it difficult to obtain an accurate power-law
exponent of the underlying distribution. The treatment is non-technical, aimed
at IS researchers with little or no background in mathematics.
