This chapter sharpens the “be skeptical of the data itself” idea.
Strong three notes:
Treat observational data as inherently biased. Not useless — just never context-free.
Always ask how missing values were handled.
There is no universal minimum sample size in statistics. “Enough data” depends on the question, variability, effect size, method, and quality of the sample.
A few more ideas are worth keeping:
Sampling bias is dangerous because more data does not fix biased data. A huge bad sample is still bad.
How was this data collected? is often more important than how sophisticated the model is.
The type of data constrains the valid analysis. Count data, continuous data, categorical data, etc. are not interchangeable.
Missingness can itself carry information. Sometimes “missing income” can become a meaningful feature rather than being blindly filled in.
Many business concepts are only proxies: loyalty, reputation, brand equity, satisfaction. You’re often measuring an approximation, not the thing itself.
The strongest synthesis here is probably:
Arguing with the data means questioning where it came from, what it actually measures, what is missing, and what assumptions are hidden inside it before trusting any result.