Linear correlation. Linear correlation represents the strength of association between two quantitative variables, without implying dependence or causality.
Propensity score. A propensity score estimates each participant’s probability of receiving a treatment based on their characteristics.
Multiple comparisons. Doing multiple comparisons increases the probability of type 1 error and of detecting a false positive by chance.
Management of non-normal data. When we are dealing with the management of non-normal data, we must use other strategies to make a hypotheses contrast.
Robust location parameters. We describe robust location parameters that are more robust to the presence of extreme values than arithmetic mean.