· Xiaojing Yang · Statistics
Correlation vs. Causation in AI Research
Correlation is useful evidence, but causal claims require stronger design and stronger assumptions.
Correlation is useful evidence, but causal claims require stronger design and stronger assumptions.
Hypothesis testing is a way to discipline claims, not a ritual for producing p-values.
相关性是有用证据,但因果结论需要更强的实验设计和假设。
假设检验不是为了制造 p-value,而是为了约束我们能说什么。