When we use data to predict something, there’s more than one way to improve the equity of that process. The one that we usually start with is setting a tolerance level for the gap between the group our predictive model works best for and the one it performs worst at


Everytime I see a stat like “25% of respondents are Black”, I see only one piece of a four-piece puzzle filled in. With only this piece, I don’t know how to use this information. …


When we want to address equity in data science, we often need to talk about power and we sometimes need to talk about money. It can be useful to think about an individual’s data like a raw resource, you could call it something cheesy like “Dataonium”. There’s a reason that…


Early in my career, I was working on a project using education data, and we were having a meeting with policymakers, school principals, and a team of researchers. When one of the principals asked a question about what assumptions were used in crafting one of the models, I was perplexed…


I want to talk to you about why you should stop saying “not statistically significant” based on sample size alone.


Learning #1: Don’t Call it “Indigenous Quantitative Methods”

A few months ago I set out to create a research brief of Indigenous Quantitative Methodologies. I wanted to start a survey of just some of the existing ways that Indigenous cultures around the world create knowledge and solve problems with data.

I had a few eye-opening experiences working with…


Does Paying or Compensating Survey Respondents Negatively Affect Response Quality or Reliability?

At We All Count, we think a lot about how to increase the equity of the data gathering process. We make a living off of the data science ecosystem and so do many of our project members and the people who read these posts. …


Talking about data equity can be tricky. Maybe you’ve been to a conference or a workshop where you encountered an idea, a tool or a process that you’re super excited about. You want to bring it up with your team on Monday but by then you’re a little hazy on…


Too often in data science, we use identity categories.

We once were hired by clients involved in a youth mental health situation where they needed to target scarce resources (why the resources were scarce is an entirely other conversation for a different time….) at providing support to young people in our community who were at risk for mental health…


You’ve heard us say it before: Define your question first, then choose a methodology. Rather than letting your methodology limit your questions, let your research questions drive the project design. For an example of how to reframe research questions and adjust methodologies, check out this post.

The We All Count…

Heather Krause

Data scientist & statistician (one of only 150 accredited PStats worldwide). Providing data science services grounded in an equity lens. https://weallcount.com

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