Just a short reminder to be wary of statistics. One statistical "error" which is ignored ever so often (by both sides) is the Simpson's Paradox.

sillymod · 2015-05-27T11:32:20+00:00

Same kind of faulty reasoning behind the pay gap.

But, for some reason with the pay gap controversy, they argue that in a society with "fair and equal opportunity", then equal numbers of men and women will go into equal paying jobs and the pay gap will disappear.

Ted8367 · 2015-05-27T10:41:22+00:00

Simpson's paradox disappears when causal relations are brought into consideration.

So you need a good causal model. That is why experts in a subject area exist.

xNOM · 2015-05-27T11:38:48+00:00

Lol, normal sjw stupidity... This is why taking down feminism in an argument is like shooting fish in a barrel. It's not a competition between visions of the future or morality even. It rarely ever gets to that level. It's just reality vs. stupidity. The most effective thing you can do as an MRA is learn some basic statistics. It's all you really need.

DavidByron2 · 2015-05-27T16:44:15+00:00

Yeah that's a great one but it only rarely comes up. No, usually the feminists lie with statistics in much more obvious ways. And in fact Simpsons isn't lying at all and you can't really manipulate the data into being Simpsons (without fabricating it entirely).

ralphswanson · 2015-05-27T17:13:44+00:00

This is why feminists are so adamant that men and women are the same (except that women are better). Women differ from men. They make different life choices based on different values and preferences, and the greater freedom that society gives women. Here, women choose careers based on interest and self-fulfillment rather than market forces, and so meet greater competition. Feminists want people to ignore these differences so they can claim different outcomes prove misogyny (but never misandry).

PeteyMax · 2015-05-27T17:54:09+00:00

A more obvious and common occurrence of this paradox is in parliamentary democracies such as Canada or the U.K. when one party gets the majority of the popular vote yet nonetheless has a minority of seats in parliament. This is because of the way the votes are distributed: the winning party, despite losing the popular vote, has many ridings that win, but only marginally whereas the party winning the popular vote has very large majorities but in only a few ridings.

The same thing can happen with the "electoral colleges" in the U.S.: a candidate can lose despite having a clear majority in the popular vote.

Whisper · 2015-05-27T23:39:44+00:00

Simpson's Paradox actually isn't a paradox at all.

It's just pointing out the fallacy of division.

The logical error here is that "Berkeley" as a whole does not have an admissions rate. Instead it is a bunch of departments, each with their own admissions rates.

When you aggregate a bunch of things that have nothing to do with each other together into a made-up thing that doesn't exist, and then attribute qualities to that made-up thing, anything is possible.

GenderNeutralLanguag · 2015-05-27T14:24:34+00:00

Here's an explanation from a Stats 101 course in a MOOC where the instructor reverses the storytelling, starting with the disaggregated numbers showing each department favoring women, and then - aha! - unveiling the aggregate figures that show women on the whole to be disadvantaged. Lies, damned lies, etc.

rg57 · 2015-05-27T20:53:48+00:00

This is on Wikipedia? Look for there to suddenly be a new "best-known real-life example" soon.

AloysiusC · 2015-05-27T21:48:41+00:00

Many statisticians believe that the mainstream public should be informed of the counter-intuitive results in statistics such as Simpson's paradox

That and a great many other things. But good luck getting it into a school curriculum. Wouldn't want people to see through the crap that the bureaucrats are relying on.

thisisnotatoaster · 2015-05-28T17:58:59+00:00

My personal favorite is anscombes quartet: Anscombe's Quartet

dontpet · 2015-05-27T10:49:06+00:00

Interring. One example is about an instance where women were thought be biased against in stem fields but turned out it was the opposite. I don't know why there is a name for this though as it seems more like the issue of statistics being complicated.

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