  A prescription is presented for a new and practical correlation coefficient,
$\phi_K$, based on several refinements to Pearson's hypothesis test of
independence of two variables. The combined features of $\phi_K$ form an
advantage over existing coefficients. First, it works consistently between
categorical, ordinal and interval variables. Second, it captures non-linear
dependency. Third, it reverts to the Pearson correlation coefficient in case of
a bi-variate normal input distribution. These are useful features when studying
the correlation between variables with mixed types. Particular emphasis is paid
to the proper evaluation of statistical significance of correlations and to the
interpretation of variable relationships in a contingency table, in particular
in case of low statistics samples and significant dependencies. Three practical
applications are discussed. The presented algorithms are easy to use and
available through a public Python library.
