Lots of kinds of data have a naturally skewed distribution.  For example, I work in water quality and most pollutants follow a log-normal distribution, which is skewed.  With good sampling procedures there will be many samples with low concentrations and fewer and fewer with higher concentrations. There are other distributions in nature like this, for example the width of cracks in bedrock.

It would be helpful for you to look up some common distributions and when they occur in the world:  Poisson distribution, gamma distribution, beta distribution.  Those are often skewed, and occur in the world in various situations.

How do we deal with skewed data? 

One option is to use summary statistics that aren't bothered by the skew.  For example using median and quartiles instead of mean and standard deviation. 

A second is to use parametric statistics that are appropriate for the distribution of the data.  That is, Poisson regression for data following a Poisson distribution, etc. 

Finally, we can transform data to make it more normal and less skewed.  For example, in looking at water quality, some parameters will be log-normally distributed and some will be normally distributed.  In this case I might simply take the log of values for those log-normally distributed parameters so that all variables are then more-or-less normally distributed.

http://www.boost.org/doc/libs/1_43_0/libs/math/doc/sf_and_dist/graphs/lognormal_pdf2.png

Skewness and bias are two different and unrelated concepts. Some data naturally follow a skewed distribution without bias. Skewness is lack of distributional symmetry. Bias is expected deviation from an estimated parameter.

See these papers

Article TO DETERMINE SKEWNESS, MEAN AND DEVIATION WITH A NEW APPROAC...

Article PERCENTILE BASED HISTOGRAM BIN WIDTH