What Should a Newly Applied Statistician Know Beyond the Standard Ciriculum

education
1

Hello,

I’m interested in the tools, methods, and practical skills that people think applied statisticians should know today—particularly those that may not be emphasized in a typical university statistics curriculum.

As a newly practicing applied statistician, the following come to mind:

  • Quarto and reproducible reporting

  • LLM’s: how to use them responsibly, and perhaps some basic theory

  • Optimization

  • Nonlinear regression

  • Categorical data analysis (e.g., Agresti)

  • Version control with Git and GitHub

  • LaTeX

  • Data privacy, confidentiality, and governance

  • Survey design

  • Reproducible analytical workflows

  • Data engineering

  • Causal inference

  • Measurement-error modeling

  • Communicating data and statistical results to nontechnical audiences

What else would you add? Are there particular tools, topics, or professional skills that you have found especially valuable in applied statistical work?

I’ve found the last one to be the most lacking. Seems like a lot of people just learn on their own. But practice in an academic setting is really not similar to a professional work setting. The story and where you start is different.

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2

A good question, and a tough one. The most important things to know, in descending order of importance, are in my current view:

  1. Statistical principles
  2. Experimental and study design (including sample survey design) and understanding biases in observational research
  3. Measurement including why not to use change scores
  4. Preservation of information in data
  5. Reproducible analysis and reporting including pre-specification
  6. Effective sample size and what limits that places on analysis; overfitting
  7. Statistical modeling subsumes all statistical tests

For general statistical analysis topics see BBR.

I would not teach \\LaTeX but rather Quarto/markdown and Typst.

Formal causal inference is far less important than experimental and study design and understanding the vast difference between prospective studies and routinely collected data.

Categorical data analysis is a special case of statistical models.

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3

Hello Frank,

Thank you for these. I noticed your list didn’t include anything on communication. Why is that? Have you had no issues with it? And I don’t just mean how to create and interpret plots. I mean, how to converse with non-statisticians. For me, it seems like a really good skill, yet neither my undergrad or my grad program had anything for it!

I think the closest thing we had was a Statistical Consulting class.

4

A clear omission on my part - thanks for pointing it out. Communication and clear, intuitive graphics that don’t give any optical illusions are both very important.

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5

Where do you think decision analysis or decision making would apply? Ultimately when I read a study that is what I am interested in, does the statistician simply provide results?

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Oh man - more super important topics that I forget to list! I think decision theory is super important and very seldom taught to statisticians. Only by understanding expected utilities and how they depend on the distribution of risk estimates can someone really understand the irrelevance of \alpha and p-values.

Then there is another all-important learning goal: Bayesian thinking.