I think I’ve found religion, or was that stats?

One of the goals I have identified for this blog is to chart some of my growth as a student over the course of my program. I feel like I need to state that from the beginning of this post, because I am going to proceed to explain my new love and obsession: methods of quantitative measurement.

I have the giddy feelings of zealot right now. You see, I am a storyteller, a writer. I am an ENGLISH teacher, for pete’s sake.  I just assumed, upon entering school again with the number one goal of doing lots of research, that I would be doing Qualitative research. Numbers, I thought, don’t tell the whole story. There is so much missing. Maybe I’d throw in some quantitative pieces, just because people like numbers, but my heart would be in open-ended questions and interviews.

What I did not realize was that the numbers were, in fact, telling stories themselves. It just seems to me that the stories are buried in the language of statistics and probability. These are, granted, somewhat complicated stories, but they are explaining the world in their own way. I have given this some thought: it’s not that I am more confident in the results or I think quantitative methods are better. I just find them intensely interesting. I learn something new and I think it about it for days: learning about ANOVA post hoc tests right now, such great names! Tukey’s HSD (for Honestly Significant Difference. Something about the “honestly” tickles me.)  and Bonferroni. There’s even one just called R-W-G-W-Q.

What I find most interesting is the rhetoric that surrounds numbers, the way this work lives out in the wild, without a statistician around to point out the nuances. Merit pay is a hot issue in Michigan right now for example. Michigan had changed its laws in order to apply for Race To the Top money, which it did not receive. Now, districts and law makers are scrambling to meet the requirements of the law, despite evidence that suggests that Merit Pay is actually not really a good way to improve student achievement. Yet here we have all of the biggest wheels turning for an expensive program that shows little or no results, despite the best data and analysis we can do. The numbers tell a story, but in this case, the listening isn’t going so well. Citing a statistic is a powerful way to put forth an argument, but I don’t think we as an audience for these arguments have a good way to question and evaluate those statistics.

In the end, I am dreaming about planned contrasts and regression. I think about it all the time. I want to talk to someone about it, but most people look at me weird and leave the room. I knew I had a problem when I got really taken with Holm’s test and looked up the original paper and read it ON MY PHONE. I just had to know.

Next up: learning R. Anyone want to help?

More fun with statistics for you:





  1. I think my main problems with numbers is a point you have hit on here perfectly. No one understands them; therefore, people can twist them to make them mean whatever they like because no one can or does call them on it.

    Then if someone does call them on it, suggesting or giving a completely different explanation of said numbers, those of us on the sidelines are now wondering what the “hell” to think? How is it even possible that the same, exact, set of numbers can give two completely different and opposite outcomes. In essences, they tell two completely different stories.

    Personally, I love numbers, but I am a closet lover because I do not wish to become embroiled in “who’s” right. *sighs* So my love of numbers is hidden, and I pull it out in dark, secretive places where no one can accuse me of manipulating the numbers to skew the outcome of said research . . . And yes . . . I enjoyed stats class. There, I’ve said it!!

    1. Dianna! So good to “hear” your voice here. I really appreciate your points and I am glad that we can share in our love of numbers. I think that this idea of being able to explain the nuances of research findings in a 140 character world is the challenge of every researcher. Not easy. Also, I think a lot about how to empower audiences to truly critically interrogate findings or use of statistics to forward an argument in a way that actually clarifies the idea, rather than a battle over “fuzzy math” or what have you. I don’t really have any answers, of course 😉

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