Statistics

One Mode, No Modes, Many Modes

I’ll close this series on averages with a quick look at the mode. Unlike the other “averages”, this doesn’t always exist, and when it is, it is not always unique. In fact, as we’ll see, sometimes we can’t be sure whether there is no mode, or many modes. How do we handle these odd cases? …

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Four Kinds of “Mean”

Last week, we looked at exactly what the mean is, referring specifically to the arithmetic mean, the one we first learn as the “average”. But just as we previously saw that there are several things called “average” (mean, median, mode), there are in fact several different kinds of “mean”. We’ll look here at the arithmetic, …

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Making the Mean More Meaningful

Last week, we started a series on averages, looking at a common list of three kinds of average: the mean, median, and mode. This time, we’ll focus in on the (arithmetic) mean, thinking about why it is appropriate for many applications; that will lead into next week’s discussion of when other kinds of mean are …

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Three Kinds of “Average”

There are three different statistics that are commonly taught as “averages”, or “measures of central tendency”, of a set of numbers: mean, median, and mode. (There are others as well, which we will get to later.) What are they? How do they differ? How do you use them? We’ll look into questions like these as …

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Normal Approximation … or Not?

(A new question of the week) A recent question (from May) about approximating the binomial distribution with the normal distribution led to some (accidental and otherwise) insights about the method. I have to solve this problem: A manufacturing company uses an acceptance scheme on items from a production line before they are shipped. The plan …

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Bayes and Virus Testing

News about testing for viruses has reminded me of a couple problems that I linked to some time ago, but never dealt with directly. The question is, given data such as the result of a (fallible) blood or swab test, how sure can we be of the results? The answer is sometimes surprising. False positives …

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