  We describe and use two different statistical approaches to try and detect
low-frequency solar oscillations in Sun-as-a-star data: a frequentist approach
and a Bayesian approach. We have used frequentist statistics to search
contemporaneous Sun-as-a-star data for coincident, statistically-prominent
features. However, we find that this approach leads to numerous false
detections. We have also used Bayesian statistics to search for evidence of
low-frequency p modes and g modes in Sun-as-a-star data. We describe how
Bayesian statistics can be used to search near-contemporaneous data for
coincident prominent features. Near-contemporaneous data were used to
circumvent the difficulties in deriving probabilities that occur when common
noise is present in the data. We find that the Bayesian approach, which is
reliant on the assumptions made when determining the posterior probability,
leads to significantly fewer false detections and those that are observed can
be discredited using a priori knowledge. Therefore, we have more confidence in
the mode candidates found with Bayesian statistics.
