Figures Don’t Lie, But Liars Do Figure
The old saying is that “figures will not lie,” but a new saying is “liars will figure.” It is our duty, as practical statisticians, to prevent the liar from figuring; in other words, to prevent him from perverting the truth, in the interest of some theory he wishes to establish.Carroll D. Wright (1889) et al
Many people seem to be waking up to the terrible, villainous fraud Bill Gates really is, admittedly cut from the same phony robber baron cloth as his ostensibly philanthropic eugenicist predecessors (‘predators’?). This is well documented for even the most cursory investigation, so I won’t belabor the matter here.
Suffice it to say, many people have recently noticed that Bill Gates has been repeatedly photographed next to a stack of books, often topped by a copy of How to Lie with Statistics (1954) by Darrell Huff. Here’s an example. This coming to one’s attention almost always leaves one rightly astonished. “What? How can that be?? That has to be a fake!” If it is a fake, then apparently Bill Gates himself endorses the forgery, because he blogged about the book back in 2015.
How to Lie With Statistics is a great introduction to a crucial topic.
By Bill Gates | March 14, 2015
I picked this one up after seeing it on a Wall Street Journal list of good books for investors. It was first published in 1954, but it doesn’t feel dated (aside from a few anachronistic examples—it has been a long time since bread cost 5 cents a loaf in the United States). In fact, I’d say it’s more relevant than ever. One chapter shows you how visuals can be used to exaggerate trends and give distorted comparisons. It’s a timely reminder, given how often infographics show up in your Facebook and Twitter feeds these days. A great introduction to the use of statistics, and a great refresher for anyone who’s already well versed in it.
That sorta reminds me of things like this – a ‘blood-spattered’ map, and a ‘hockey-stick curve’ in the bottom right-hand corner (below). How about you…?
The Big Lie
A big lie (German: große Lüge) is a propaganda technique and logical fallacy. The expression was coined by Adolf Hitler, when he dictated his 1925 book Mein Kampf, about the use of a lie so “colossal” that no one would believe that someone “could have the impudence to distort the truth so infamously”. Hitler believed the technique was used by Jews to blame Germany’s loss in World War I on German general Erich Ludendorff, who was a prominent nationalist and antisemitic political leader in the Weimar Republic.
The Bigger Picture [Lie]
Here is one outrageous example (first 45 seconds) of Bill Gates magically inventing numbers (“33%…exponential growth”) to advance his agenda. What’s particularly bizarre in this example is that Bill Gates is a college dropout and no doctor or medical or health expert, but Sanjay Gupta is a very well respected doctor, yet watch how Dr. Gupta hangs on Bill Gates every word. Notice how he sounds like a child throwing a tantrum, unaccustomed to being challenged, or not getting his way.
My point is not simply to demonstrate what a bombastic scoundrel and preposterous liar Bill Gates is. He does a fine job on his own. Rather, I have incessantly drawn people’s attention to the bigger picture here, and in an attempt to get people asking appropriate questions, I’ve focused particularly on the number of active hospitalizations – or significant lack thereof. We were told (threatened) from the start that we might inevitably overwhelm the health care system, and that 1.1-2.2 million Americans could die, a catastrophe worsened by hospitals overrun with COVID-19 zombies, and ‘bodies piling up in the corner’.
Not only has the active hospitalizations data point been entirely occluded from public view (have you ever heard anyone mention it once?) when you can find it – for example in Montana – it’s almost always astonishingly miniscule (13 active hospitalizations in MT as of 4/23/20). The response to which is usually something like, “That’s because social distancing worked to slow the spread.” Another myth (i.e. outright lie) which is false, not to mention misleading since the original models factored in social distancing, still predicting millions might die. If you’re peddling fear, no wonder you wouldn’t include that data point in your ‘number games’.
The Numbers Don’t Lie
The recent COVID-19 Antibody Seroprevalence1234 study proves the mortality rate is more like 0.0741% – even less if (when) we adjust our inflated mortality rate down, most likely by about 88%, like Italy. Those numbers are more on par with this season’s flu, only far less virulent (deadly). Many experts have voiced ongoing concerns567 about the inaccuracy and fallibility of the modeling data, as well as the ‘real’ data. Indeed, if one is courageous enough to confront the facts, one quickly discovers that this has been little more than a premeditated PSYOP, and any possible argument to the contrary is quickly overwhelmed by the facts.
A [Data] Picture Is Worth A Thousand Words
Perhaps the most astounding data point now is the US explosion of cases, far outpacing the rest of the world. At a glance, we might simply write this off as a case of testing – i.e. we’re testing more than all other countries, according to the official WHCTF (Trump) narrative. But while that might (or might not) explain the extraordinarily disproportionate number of US cases, it does not explain the dramatic disparity of the death count.
Thank you for reading this post, and may I suggest appropriate action, along with further investigation of these matters. I hope for posterity’s sake and our own, that we can and will rise to the occasion, to prevent further catastrophe.