**If you want to verify whether a relationship
you have observed between two variables is real, you have a variety of
statistical tools available. A problem arises, though, when you have
measured these variables without much precision, using categorical
measurement. The solution is a two-way chi-square test, which, among
other things, can be used to make unsubstantiated assumptions about the
characteristics of people you have just met.**

"Identify
Unexpected Outcomes" [Hack #15] used the *one-way
chi-square test* to make police scheduling decisions based on
whether equal numbers of crimes were committed at different times of
day. That tool works well to solve any analytical problem when:

The data is at the categorical level of measurement (e.g., gender, political party, ethnicity).

You want to determine whether there is a greater frequency of scores in certain categories than would be expected by chance.

You face another common analytic problem when you're curious to
know whether two categorical variables are *related* to
each other. Relationships between categorical variables can be examined
with the handy *two-way chi-square* test.

If two variables are measured at the interval level (many scores are possible along a continuum), the correlation coefficient [Hack #11] is the best tool to use, but it doesn't work well with categorical measurement.

We make assumptions all the time about relationships between these sorts of variables. Many of our common ...

Start Free Trial

No credit card required