Statistical terms: what they really mean

Multicolinearity— they all look the same
Heteroscedasticity— the variation varies
Attenuation— being too modest
Overfitting— too good to be true
Confounding— nothing is what it seems
P-value— it’s complicated

Sensitivity analysis— tried a bunch of stuff
Post-hoc— main analysis not sexy enough
Multivariate— oops, meant to say multivariable
Normality— a very rare shape for data
Dichotomized— data was tortured
Extrapolation— just guessing

Linear regression— line through data points
t-test— linear regression
correlation— linear regression
ANOVA— linear regression
ANCOVA— linear regression
Chi-square test— logistic regression
Deep learning— bunch of regressions

Advanced stuff:
Non-convergence— computer says no
Heywood case— science fiction
Bootstrap standard errors— could not do the math
Robust standard errors— pretending to be cautious
Shrinkage— regularization
Regularization— couldn’t get large enough dataset