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One of the central tasks of this course has been to unpack the ways in which work in today's economy has changed over time. Often, when we talk about this topic, one of the first things that comes to mind is the role of technology in transforming work. Over the past few years, we have seen an explosion in what's known as the on-demand economy. It's visible everywhere. We as consumers are able to have our food delivered, grab a ride home, or even have someone run their errands, all purchased and arranged in a matter of minutes from our smartphones. Our colleague John McCarthy talks in more detail about these new ways of organizing work in a different video lecture in this course. What we're going to talk about now is how these employment arrangements are connected to two issues-- one, how we think about workers' rights in the on-demand economy, and two, how technology can promote new forms of worker advocacy in settings where gigs, not full-time jobs fo...

The central limit theorem is absolutely remarkable. It is a very deep result, and highly nontrivial and non intuitive. There's no apparent reason why this random variable here, a standardized version of the sum of random variables, should have an approximately normal distribution. Furthermore, it is very useful, and one key reason is that it is universal. It doesn't matter what the distribution of the X's is. No matter what the distribution is, still in the limit, this standardized version of the sum is going to behave like a normal random variable. And if we wish to apply it to particular examples or models, the only thing that we need to know about the distribution of the X's are the corresponding means and variances, as we're going to see in multiple examples. When we apply it, it turns out to be very accurate, and it is also a very nice computational shortcut. Even if we knew, in detail, the distribution of the X's, in order to calculate the di...