# as seen in Figure 6.1a, can derail central matching. The MLE method is more stable, but pays the price of possibly increased bias.
# 2. Should the variance associated with this weighted.mean T stat value be based on the variance of the T stat scores, the variance of the mean, or both?
# 3. Why not estimate the SD of the distribution directly, without the need to provide manual variance estimations? Because then each weigted mean value
# is treated as if it came from the same population?
# 4. library(Hmisc) -> wtd.var(x, weights)
# 5 . Variance estimation is based on before the centralization, is this correct? Yes, independent
# 6. Is centralization actually correct at all since the scores come from a different population? Yes, if assumed they come from a normal distribution
# NEW: Estimate variance of T score estimate with Welch corrected df (test$parameter)
