consulting

I would love to chat with you about your statistics questions!

what i can help with

  • Suggesting methods to analyze your data, offering relevant code samples, links to resources, etc
  • Giving you feedback about a data analysis or helping you respond to reviewers
  • Designing experiments or figuring how to approach a new project
  • Programming in R or Python, organizing your code and data, making a replication package
  • Digging through data together

Quick note: if you need someone to do your entire analysis or write your methods section, you probably want an ongoing collaborator rather than a consultant.

before we meet

If you do some prep work before we meet, it makes consulting a lot smoother. Take a few minutes to jot down answers to these questions (skip any that don’t apply):

  • What are you trying to learn or do? Tell me in plain language what you want to learn. Don’t worry about the technical details yet.

  • Why does this matter? Help me focus my efforts by explaining why your work is important or the decisions you will make based on your analysis.

  • Who is your audience? Where are you trying to publish? What do your stakeholders care about? How much statistical background do they have?

  • What kind of constraints do you have? Is it more important to get a correct answer or a fast answer?

  • What have you tried so far? Give me two versions: explain once like I’m your friend who doesn’t know any statistics, and then a second time with all the technical details.

  • What data do you have? Please create a data dictionary that describes each column in your data and what each row of data represents.

Tip

Sending me a detailed data dictionary is the best way to get fast help.

Once you’ve got these answers, grab a time on my calendar and email me what you wrote!

If you want to dig through data together during our meeting, please organize your data ahead of time. It’s great to have data in .csv files in tidy format.

If you need help making this happen, take a look at the Data tidying chapter of R for Data Science or the Data Wrangling chapter of Python for Data Analysis.

For more details, see Jeff Leek’s guide on sharing data with a statistician.