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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After over 25 years of helping researchers hone their statistical skills to become better data analysts, I’ve had a few insights about what that process looks like.
The one thing you don’t need to become a great data analyst is some innate statistical genius. That kind of fixed mindset will undermine the growth in your statistical skills.
So to start your journey become a skilled and confident statistical analyst, you need: (more…)
A normal probability plot is extremely useful for checking normality assumptions. It’s more precise than a histogram, which can’t pick up subtle deviations. And yet it doesn’t suffer from too much power from large samples with tiny departures from normality or too little power from small samples with large departures from normality, as do tests like Shaprio-Wilkes.
The biggest problem with a normal probability plot is that it’s hard to read, especially if you’re not used to them. So let’s take a moment and walk through exactly how they work and what they tell you.
There are two versions of normal probability plot: Q-Q and P-P. I’ll start with the Q-Q. (more…)
The first real data set I ever analyzed was from my senior honors thesis as an undergraduate psychology major. I had taken both intro stats and an ANOVA class, and I applied all my new skills with gusto, analyzing every which way.
It wasn’t too many years into graduate school that I realized that these data analyses were a bit haphazard. (Okay, a LOT). And honestly, not at all well thought out.
A few decades of data analysis experience later, I realized that’s just a symptom of being an inexperienced data analyst.
But even experienced data analysts can get off track. It’s especially easy with large data sets with many variables. It’s just so tempting to try one thing, then another, and pretty soon you’ve spent weeks getting nowhere. (more…)
Have you ever wondered whether you should report separate means for different groups or a pooled mean from the entire sample? This is a common scenario that comes up, for instance in deciding whether to separate by sex, region, observed treatment, et cetera.
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When you draw a graph- either a bar chart, a scatter plot, or even a pie chart, you have the choice of a broad range of colors that you can use. R, for example, has 657 different colors from aliceblue to yellowgreen. SAS has 13 shades of orange, 33 shades of blue, and 47 shades of green. They even have different shades of black.
You have a wealth of colors, but you can’t use all of them in the same graph. The ideal number of colors is 2.
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