We're all different sizes, and there is no one-size-fits-all, but when it comes to the chosen size it turns out that that size mostly fits men better.

Caroline Criado Perez in her book Invisible Women shares how so much of the designed world fits men better than women — examples include piano keyboards that make playing more technically difficult and more prone to injury for smaller hands, stab vests that didn't take account of breasts, crash test dummies for car seats tested only with males, toilets that always lead to queues, phones that don't fit female hands or pockets, and voice recognition that picks out male voices more easily.

It's a stark reminder of how we need to do more to design with more of the world in mind rather than the default reference male. 

Read more: The deadly truth about a world built for men – from stab vests to car crashes.

See also brilliance bias and the curb-cut effect.

One-size-fits-men

When we notice two things change with each other — sunny days and ice cream sales, or player height and goals scored — it's natural to think that one thing is the cause of the other. And it's only a small step to consider that if you do more of one, you'll get more of the other. But this instinct and our natural sense-making behaviours can easily lead us astray. Just because two things change together — are correlated — doesn't mean that one causes the other: correlation is not causation.

You might reasonably notice a correlation between the number of children in a household and the number of storks nests on the roof of houses in Denmark or Holland. Fortunately, while the two may be correlated it's not hard to see that it's most likely not the storks that are causing the larger families and instead another factor such as the size of the house that is the more likely cause. As such it's a handy reminder that correlation is not causation.

This is also the subject of my favourite xkcd on correlation, and another on cell phones to drive the point home.

For more see the excellent spurious correlations.

The stork nest example is from Darrel Huff's How to Lie with Statistics, as is the example on Sampling Bias.

Correlation is not causation

Chesterton's fence put simply is: Don't take a fence down unless you know why it was put up.

It is tempting, in a spirit of progress, to want to do away with old designs, laws, policies, or institutions that no longer help. To think, perhaps, that the people that made them weren't maybe as smart as we are, or seeing things in the right way. Yet institutions, policies, or fences, weren't usually made by accident — they require effort and action, even putting up a fence. Chesterton's fence, after GK Chesterton, is a reminder that before we remove the old and perhaps replace it with the new we would do well to at least understand why the original was put in place.

Once we understand why the fence was there in the first place if we still think it's of no use, then by all means pull it down.

Read more on Chesterton's fence, including its origins.

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Chesterton's fence