Last time we saw how we can use the chi-squared distribution to test whether a sample of values is consistent with pre-supposed expectations. A couple of months ago we took a look at Student's t-distribution which we can use to test whether a set of observations of a normally distributed random variable are consistent with its having a given mean when its variance is unknown.

# Category: hypothesistesting

## Time For A Chi Test – a.k.

A few months ago we explored the chi-squared distribution which describes the properties of sums of squares of standard normally distributed random variables, being those that have means of zero and standard deviations of one.

Whilst I'm very much of the opinion that statistical distributions are worth describing in their own right, the chi-squared distribution plays a pivotal role in testing whether or not the categories into which a set of observations of some variable quantity fall are consistent with assumptions about the expected numbers in each category, which we shall take a look at in this post.

Whilst I'm very much of the opinion that statistical distributions are worth describing in their own right, the chi-squared distribution plays a pivotal role in testing whether or not the categories into which a set of observations of some variable quantity fall are consistent with assumptions about the expected numbers in each category, which we shall take a look at in this post.