Finite and Infinite Statistics
Finite statistics are statistics calculated from finite sets. Basically, you have a sample that you’re using to make a calculation (like the sample variance). If you have a countable number of data points in your sample, what you end up with is a finite statistic.
On the other hand, infinite statistics are those calculated from infinite sets. For example, a probability density function has, for practical purposes, an infinite number of data points under its curve.
The normal distribution is another example of an area that uses infinite statistics: the z-table on this site lists just a few hundred points, but technically the table has an uncountable number of points on it (e.g. z=2.1 is listed, but z = 2.1249865 is not). This is for a couple of reasons:
- Space: there simply isn’t room on any page in existence for a table of infinite values!
- Practical Purposes: Even if you could list every possible z-value, there comes a point where the values are so similar, a finite set is “good enough”. Take a look at this snapshot from the table:
Any value between 3.7 and 3.8 would also be an area of 0.4999, so there’s really no point in listing them all.
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