Bullwhip Errors

Statistics Can Do That When Modeling

Do you know why the climate cult is wrong? Their models suck.

A good friend alerted me to this article and I am so ticked at myself for not thinking of it first. My guess is when they first did COVID modeling that showed a large percentage of the humans on Earth dying, they had the same statistical errors in modeling. But, the ends justify the means.

For what it’s worth, masks do not work and 99.8% of the world population amazingly survived Covid now that they are trying to manufacture another scare.

The solutions to the fake climate crisis aren’t about enabling you as a human to live a better life and have more control over it. The solutions are about enabling centralized planners to have more control over resources so they can tell you what to do.

This is the core meat of the article:

I quickly realized that the goal of the project, to forecast accurately the temperature long-term, was impossible because small errors in data inputs could result in huge forecasts errors. Equally important was that errors compounded so quickly that it caused the error ranges to explode. The results were junk. As an example, what good is a temperature forecast with an error range of plus or minus one hundred degrees?

They call this the bullwhip effect when it is in operations management as it relates to supply chains. It works in statistical modeling too. Small initial errors compound over time in the model turning into gigantic numbers.

Don’t believe me? Try it with stock market returns or bond returns. Intentionally input some bad data by small degrees and see what the result is in 30 yrs.

Another way to think about it is the old question, “Do you want to receive a penny doubled every day for 30 days, or a million dollars today?”

Next thing the climate people will tell me is that Joe Biden isn’t lying.