Editor's note: Read Part 1 of this blog here.
When I was a statistics student “back in the day,” I learned several important precepts that I can recall as if yesterday. The first was the unbridled joy of working with bell curve-distributed data. Bell curve – normal or Gaussian – distributions not only seemed applicable to any number of measurable traits, they were also quite tractable mathematically. Indeed just two parameters, the mean and variance, described all that needed to be known about a normal distribution.
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