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 …

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  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 …

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  3. 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 …

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  4. Prediction vs. Inference

    So far, our statistics material in this course has fallen into two buckets. The first, and most straightforward, is descriptive statistics, that is, just describing what our data looks like---mean, median, correlation, that kind of stuff. The second is "inferential statistics," that is, use of statistics to make inferences about …

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