Recent innovations in data science raise troubling questions about discrimination and loss of privacy. 
In this training, you will see how statistics are not cold, hard numbers, but reflections of the perspectives and biases of those collecting the data.
We’ll discuss several case studies that show how real the harms can be. You’ll learn about some of these ethical problems and some possible solutions.
Webinar Date & Time: May 19, 2026 at 3pm US ET.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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Outliers. There are as many opinions on what to do about them as there are causes for them.
But there is a lot of bad advice out there about what to do with outliers.
In this training, we’ll take a step back and explore how to think about outliers so you can make good decisions based on your data and model. You’ll learn the different types of outliers and methods for figuring out which type you have. You’ll also learn how to determine whether, and how, they’re influential, and what to do about it.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
From the last posts in this series, you should feel comfortable using Stata’s data editor, changing values and types, and creating new variables.
We’ll now teach you to make your variables more approachable by adding labels.
The image below shows label information for the foreign variable.
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Once you’ve imported, examined, and cleaned your data, a common next step would be to make some visual displays or graphs. In this article we’ll go over the details of creating, naming, saving, and exporting graphs in Stata.
We will do all of this using syntax, rather than Stata’s “Graphics” menu. If you want a quick lesson on using the menus to make graphs in Stata, check out this article. (more…)
This month we are featuring a 9-module software tutorial by Kim Love: An Introduction to Data Analysis using R.
It’s perfect for people who:
- have never used R before
- need to refresh their R skills after not using it for while
- have figured out R on their own and would like a more systematic tutorial
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Stata makes it a breeze to edit or clean your data. If you’re unfamiliar with using data sets in Stata, check out these blog posts to get a good grasp on importing and browsing data in Stata.
For this tutorial we will be using Stata’s “auto” data set. If you haven’t loaded it in yet, type
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