Bayes factor analysis
Bayes Factor (BF) refers to the ratio of the marginal likelihood of
alternate hypothesis to the likelihood of null hypothesis, given the
data. The measure compares the alternate against the null hypothesis,
rather than limiting the inference from the null hypothesis evidence
(Jarosz & Wiley, 2014). In the present study, we used the Bayesian
paired t-test with a Cauchy prior scaled to 1. Under this scheme, BF
values below 1 suggests the absence of effect and evidence in favor of
the null hypothesis, BF values between 1 and 3 provide anecdotal
evidence in favor of the alternate hypothesis, while values between 3
and 10 provide substantial evidence and values above 10 provide strong
evidence in favor of the alternative (Jeffreys, 1998). We calculated BF
for right-tailed paired t-test (connectivity(controls) >
connectivity(cases)) or (connectivity(Middle-aged) >
connectivity(elderly)) for all the frequency bands. We used the MATLAB
toolbox on BF by Bart Krekelberg (Krekelberg, 2021) based on (Rouderet al. , 2012).