Yes, in general the state of the art is more advanced than looking at correlations.
You just need to learn when using correlations makes sense. Don’t assume that everyone is using correlations blindly; Statistics PhDs most likely decide whether to use them or not based on context and know the limited ways in which what the say applies.
Correlations make total sense when the distribution of the variables is close to multivariate Normal. The covariance matrix, which can be written as a combination of variances + correlation matrix, completely determines the shape of a multivariate Normal.
If the variables are not Normal, you can try to transform them to make them more Normal, using both univariate and multivariate transformations. This is a very common Statistics tool. Basic example: Quantile normalization.
Yes, in general the state of the art is more advanced than looking at correlations.
You just need to learn when using correlations makes sense. Don’t assume that everyone is using correlations blindly; Statistics PhDs most likely decide whether to use them or not based on context and know the limited ways in which what the say applies.
Correlations make total sense when the distribution of the variables is close to multivariate Normal. The covariance matrix, which can be written as a combination of variances + correlation matrix, completely determines the shape of a multivariate Normal.
If the variables are not Normal, you can try to transform them to make them more Normal, using both univariate and multivariate transformations. This is a very common Statistics tool. Basic example: Quantile normalization.