Statistics, predictions, and lies

Statistics. Perhaps the most overused and misused statistical concept used today is 'average'. Let's say that a measure of interest had the following number over the past ten years, from first to most recent: 10, 4, 10, 4, 10, 4, 10, 4, 10, 4. The average is pretty simple -- 7. Let's say that the measure of interest was degrees centigrade on the 1st of February in each of those years. If you were asked to name the typical temperature on February 1st, or to estimate/predict what the temperature would be on February 1st in the coming year, what would your answers be?

Predictions. Let's say that the mortality rate -- deaths per 100,000 people -- grew consistently over the last ten years, from 8.1 to 9.0, increasing by 0.1 each year. What was the average? What would you predict would be the mortality rate in the coming year -- absent an unusual event such as a pandemic? While the average is 8.55, the best estimate for the coming year would be 9.10. If 100,000,000 people lived in that country (and the population was not growing), would you expect 8,550 deaths or 9,100 in the coming year? Better yet, which of these two estimates would you believe? Which of the two is more credible? Which might actually be a lie, false news, or a misapplication of simple math?

Lies. Where does one begin when we have outright lies, as promulgated by the infamous Obama healthcare advisor, Jonathan Gruber (e.g., “If any American really believes that Obamacare is going to control costs, I've got some real estate in Whitewater, Arkansas I'd like to sell them.”). Gruber is the million-dollar Harvard consultant on the Affordable Care bill, who claimed people are too stupid or ignorant to make their own choices, and that decisions best be left to elite experts like him, or the government. Or, take a lack of transparency or candor, as Nancy Pelosi condescended to Americans, "You have to pass the bill so you can see what's in it."