No. of Recommendations: 4
There are a lot of issues floating around here, and I certainly don't want to scare anyone off.

The New Orleans Times-Picayune ran a piece last Friday attempting to answer a question the entire world has been asking: Should ice cream be blamed for murders? “The correlation between homicides and ice cream sales—when ice cream sales increase, the rate of homicides also increases—has long been a topic in statistics and science classrooms,” writes John Harper, citing several recent cases of ice cream-related crime.

In the second paragraph of his piece, Harper thankfully reminds readers that correlation is not causation, and that ice cream’s relationship to homicide is a mere statistical coincidence. The idea that frozen treats cause crime is obviously ridiculous, unless you’re talking about that addictive Cocaine Chip ice cream I’ve heard so much about. But it does stand to reason that ice cream sells better in warm weather, and there is in fact plenty of evidence to suggest that murder rates rise when temperatures rise.

Using a variety of statistical methods, including transformations (square root, logarithmic, etc); different regression/correlation methods (simple linear, multiple regression, principal component analysis); and things like careful selection of dependent and independent variables, I can force pretty gnarly data to fit some sort of predictive relationship. Anyone who doesn't believe that has never been around a statistic driven project in need of demonstrating a statistically meaningful outcome. So correlation coefficients don't really impress me.

The first question you should ask when viewing statistics is "Is the data normally distributed?". That is going to tell you a lot about appropriate methods. Another good question is, does the thing I'm trying to predict fit within the previously observed outcomes? Assuming a normal distribution, does it fit within 2 standard deviations of the mean? What happens to the confidence interval as I get outside the previously observed outcomes or away from the mean?

Predicting what the market will do in the next quarter or year is challenging enough, regardless of the statistical method used. If you had told me last July we would be looking at a rate cut this July, I would have laughed at you. If it wasn't hard, and subject to a lot of randomness, then there would be a lot more rich people, right? Monte Carlo analysis and simulators like FIRECalc are intended to help by handling longer term. Monte Carlo simulation is often used when simple statistics don't work well, or suffer from things like the previously described sequencing risk.

For those using something like FIRECalc, sequencing risk is somewhat mitigated if you follow the advice to have 2-3 years of cash needs in cash or a near cash equivalent.

For folks like me, further from retirement, the big number to keep in mind is the annual inflation adjusted stock market return/Compound Annual Growth Rate (CAGR). Right now, I don't care if the SWR is actually 3.8, 4.0 or 4.2% because I'm far enough from retirement it has little effect on what I need to do. I do care of the CAGR over the next ten years is 6%, 7%, or materially higher or lower. If its a lot lower than 6%, I'm either going to have to work longer, or come up with a different (riskier?) asset class that will do better.
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