1. # Week 4 recap: Total Probability Rule

The probability lecture this week kind of hit a wall at one point in our Bayes Rule example. Here's a clearer explanation.

Remember, we had an example problem involving figuring out the posterior probability of someone being drunk, given that they blew a positive result on a breathalyzer.

The place …

2. # Why Statistics for Lawyers?

The brunt of this course will be devoted to statistics and exploratory data analysis.

Exploratory data analysis is just looking at data to see what you see. We will spend some time, for example, looking at how to see the shape of data and what that can tell you about …

3. # Hypothesis Testing: Conceptual Introduction (draft)

Now that we understand distributions and the central limit theorem, we’re in a good position to make sense of the notion of a hypothesis test. It’s actually very simple.

Suppose you do an experiment. Let’s say you want to find out whether a company is engaging in …

4. # Abel and Baker Redux: Probability and Description

Right at the beginning of the F&L readings for this week, you saw a well-known probability puzzler/"paradox." I'm going to risk angering the copyright gods and quote it in full here:

Assume that boys and girls are born with equal frequency. Mr. Able says, “I have two children …

5. # P-Values and Bayes Rule

Recall from the previous lesson what a p-value is: it’s the probability of observing a value of your statistic as extreme (as far away from the null hypothesis statistic) as you in fact observed, if the null hypothesis were true.

In other words, if you’re doing a (two-sided …