There's an ongoing debate in the academic community about whether Calculus is a necessary pre-requisite for teaching Statistics. But in age of ubiquitous computing resources (not to mention open source programming languages like R), there's a fair argument to be made that all you *really* need is simulation. However complex the statistical proposition, you can always find useful information about its properties simply by generating a bunch of random numbers and seeing what happens.

The same applies to education: rather than focusing on probability and calculus, students can simply see what happens when you flip a coin or roll dice, and how the statistics converge in the long run. That's the premise behind Seeing Theory, a visual introduction to probability and statistics created by Daniel Kunin, a senior at Brown University. It starts with probability: for example, rolling a fair die to show the long-term average is 3.5. There's also a neat method of estimating the value of pi by counting random points falling in (or out) of a circle, or this demonstration of the Central Limit Theorem that drops random samples out of a skew distribution and showing that their mean has a Normal distribution.

There's lots more to explore (though some of the units are still being developed), and it's a great calculus-free way to get a budding statistician interested in the topic.

That's all from us for this week. Enjoy your weekend, and we'll see you back here on Monday!

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