In today’s Uncertainty Wednesday we are putting some of the ideas from the last few weeks together: we are looking at the behavior of the sample mean of a fat tailed distribution. To do this we will again use a bit of Python code. Unlike our first sample mean example where we looked at the roll of a die, we will need some help here to draw samples from a more complicated distribution. Thankfully the Python ecosystem has the wonderful SciPy libraries, which if you don’t know already you should...

In today’s Uncertainty Wednesday we are putting some of the ideas from the last few weeks together: we are looking at the behavior of the sample mean of a fat tailed distribution. To do this we will again use a bit of Python code. Unlike our first sample mean example where we looked at the roll of a die, we will need some help here to draw samples from a more complicated distribution. Thankfully the Python ecosystem has the wonderful SciPy libraries, which if you don’t know already you should...

Today’s Uncertainty Wednesday will be quite short as I am super swamped. Last week I showed some code and an initial graph for sample means of size 100 from a Cauchy distribution. Here is a plot (narrowed down to the -25 to +25 range again) for sample size 10:And here is one for sample size 1,000:Yup. They look essentially identical. As it turns out this is not an accident. The sample mean of the Cauchy distribution has itself a Cauchy distribution. And it has the same shape, independent of h...

Today’s Uncertainty Wednesday will be quite short as I am super swamped. Last week I showed some code and an initial graph for sample means of size 100 from a Cauchy distribution. Here is a plot (narrowed down to the -25 to +25 range again) for sample size 10:And here is one for sample size 1,000:Yup. They look essentially identical. As it turns out this is not an accident. The sample mean of the Cauchy distribution has itself a Cauchy distribution. And it has the same shape, independent of h...