In the evaluation of correlations, it can always be informative to know the confidence interval for r, with all caution towards p-value interpretation.<p>Surely, correlation provides information on association rather than cause and effect (causation should rather be modeled with Granger and other regression models). Sample sizes and variances will certainly contribute to different p-value outcomes. This is because p-values reward low variance more than the magnitude of impact (Type I&#x2F;II error etc.). If you have p-values, better report them and add a footnote on how to interpret them.