mathematics//statistics
Statistics is the machinery for reasoning from incomplete, noisy observations without pretending uncertainty disappeared.
Statistics is the machinery for reasoning from incomplete, noisy observations without pretending uncertainty disappeared.
Description summarizes the sample; inference makes claims about the process that generated it. Confusing the two is how dashboards become scientific evidence by accident.
Dispersion and joint variation are quantified by variance, covariance and the covariance matrix.
A model's assumptions are part of its output. Independence, sampling, missingness, and measurement quality often matter more than the sophistication of the estimator.
Methods such as the bootstrap quantify variability by recomputing a statistic across resampled datasets rather than demanding a convenient closed-form distribution.
More data narrows random error. It does not automatically repair selection bias, leakage, bad labels, or a question the data never measured.