How do you use domain knowledge to interpret statistics?

Drawing from personal experiences, domain knowledge has often served as a bridge between raw numbers and actionable insights. I've found that understanding the nuances of a specific industry or subject can mean the difference between making a sound decision based on statistics or potentially heading down a misguided path. For instance, while teaching master's level students, it's evident when a student interprets results purely from a mathematical standpoint versus one who weaves in domain knowledge. The latter invariably offers richer and more impactful insights. In real-world AI model building, domain knowledge has been invaluable in guiding feature engineering, interpreting model outputs, and even in explaining outcomes to stakeholders.

Statistics is understanding if results are significant at a p value of 0.05 or p value of 0.01. Domain knowledge is understanding, where you can afford to be wrong at a significance of p = 0.05 or p = 0.01

Here are some steps to consider: Understand the Context, Define Variables, Check Data Quality, Choose Appropriate Statistical, Methods, Interpret Descriptive Statistics, Consider Statistical Significance, Account for Confounding Variables, Explore Trends and Patterns, Verify Assumptions, and Communicate Findings Effectively. Overall, integrating domain knowledge with statistical analysis is essential for accurate interpretation and meaningful insights. It allows statisticians and researchers to contextualize the data, identify patterns, and draw conclusions that are relevant to the specific field of study.

A statistics or data science student, upon learning the tools of the trade, may very well feel able to draw insights about anything: while it's true that statistical tools can be applied to all sorts of data, knowing what the data mean, how they're produced and what are the possible implications makes all the difference. Producing numbers as a result of a statistical analysis is easy, making sense of them is hard.

In a work setting, acquiring domain knowledge will often mean that you'll be talking with people with a different skill set from you. The jargon may be different, the same underlying concepts may be seen from another point of view. Your job is to learn from them in order to deliver insights that they can understand and act upon. My advice: stay humble.

just work for a long time in one field - and focus on depth as opposed to breadth - that is one way to acquire domain knowledge

Acquiring domain knowledge takes time and deliberate effort. It requires educating yourself, being educated by others, asking questions, having real conversations with people who challenge your ways of thinking, searching for patterns, and making mistakes. It can't be rushed, takes commitment, and can humble you in ways that are really challenging (but wonderful for growth!). There is also no amount of classroom learning that can outperform first-hand experience with something, so my suggestion is to get as involved as you can, as soon as you can, and in as many ways as you can.

Do not underestimate the power of understanding research methods, data quality, and context. Many statistics come from datasets that are biased or incomplete. Lots of organizations have research agendas that skew their analytical choices and influence how the data is presented. Often, correlation is conflated with causation. Measurements may not be valid or reliable. Sometimes analyses are not appropriate for the sample size. Always ask questions about the data and methods when you see statistics! A good organization will provide this for you. Finally: p-values are not the end-all be-all. A result can be statistically significant but have a meaningless effect. Likewise, a result can be non-significant, but have a very meaningful effect.