  We apply the Bayesian framework to assess the presence of a correlation
between two quantities. To do so, we estimate the probability distribution of
the parameter of interest, $\rho$, characterizing the strength of the
correlation. We provide an implementation of these ideas and concepts using
python programming language and the pyMC module in a very short ($\sim$130
lines of code, heavily commented) and user-friendly program.
  We used this tool to assess the presence and properties of the correlation
between planetary surface gravity and stellar activity level as measured by the
log($R'_{\mathrm{HK}}$) indicator. The results of the Bayesian analysis are
qualitatively similar to those obtained via p-value analysis, and support the
presence of a correlation in the data. The results are more robust in their
derivation and more informative, revealing interesting features such as
asymmetric posterior distributions or markedly different credible intervals,
and allowing for a deeper exploration.
  We encourage the reader interested in this kind of problem to apply our code
to his/her own scientific problems. The full understanding of what the Bayesian
framework is can only be gained through the insight that comes by handling
priors, assessing the convergence of Monte Carlo runs, and a multitude of other
practical problems. We hope to contribute so that Bayesian analysis becomes a
tool in the toolkit of researchers, and they understand by experience its
advantages and limitations.
