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The single-principal-component approach to expressing ideology and the resulting lack of dimensionality in the political system are preventing the expression of statistical variance in political thought that is so essential to a democratic society.

Through my own experience and speaking to others, I’ve found that introductions to and discussions of regression inadequately describe its assumptions and underlying logic.

Let’s talk about linear regression. Modeling a prediction with a linear relationship is about as vanilla and core as regression can get; and while students are often introduced to linear regression from a very young age, rarely are the mechanics of regression truly understood.

Risk analysis is a quantitative field that cannot quantitatively answer its own fundamental problem with rigor. Some may find this to be epistemologically unsatisfying. However, risk analysis is absolutely necessary and crucial for industrial management, economics, engineering, and finance, in spite of its mathematical imperfections.

In the end these mathematical imperfections, and the field’s resultant ever-expanding search for improvement and invention, make risk analysis and the philosophy of statistics all the more beautiful.

It’s so difficult to find that perfect match – your Mr. or Mrs. Right, or your best possible better half. People observe that nobody is perfect, and some even start to believe that attractive traits are negatively correlated. In fact, Slate Magazine’s “Explainer” column had as its 2011 Question of the Year:

“Why are smart people usually ugly?” – Slate Magazine, 2011

According to the expert statistician Steven Wright, 42.7% of statistics are made up on the spot. While this is intended as ironic humor, there is a dark truth behind that sentiment. Statistics can bias, skew, and muddle our vision of the truth, and is even sometimes used intentionally to do so…