Problems with Common Core and EngageNY -- statistics edition

Last week we came back to the topic of Common Core standards thanks to this extraordinary post by Gary Rubinstein which uncovered some appalling quality control issues.

The problems involved EngageNY (arguably the gold standard in CC based lesson plans). Rubinstein focused on algebra; I decided to check out the sections on statistics. What I found was uniformly bad. I’m going to focus one section [Lesson 30:  Evaluating Reports Based on Data from an Experiment], but the general concerns apply to all of the sections I looked at.

When explaining a highly technical subject to younger students, we sometimes go too far in an effort to smooth off the edges. We lose precision trying to stick with everyday language and we leave out important details because they greatly complicate the picture. When we try to communicate scientific concepts, there will always be a trade-off between being accurate and being understandable.

This is invariably a judgment call. What's more, it is a judgment call that varies from subject to subject and from audience to audience. We can argue about where exactly to make the cut, but we can’t really say one position is right and the other is wrong.

That’s not what we’re talking about with EngageNY. The authors like to throw in impressive-sounding scientific language and wordy constructions but not in a way that makes the writing more precise.

For example:

Students should look to see if the article explicitly states that the subjects were randomly assigned to each treatment group.  This is important because random assignment negates the effects of extraneous variables that may have an effect on the response by evenly distributing these variables into both treatment groups.

“[N]egates the effects of extraneous variables that may have an effect” is not a phrase that the typical high school student will find particularly informative, but this paragraph also manages to be not-quite right. That “evenly” seems to suggest that the distribution (rather than the expected distributions) of non-treatment variables will be identical, while the part about “distributing” variables just seems odd.

At best, these lessons are sloppy; at worst, they’re wrong. Take this for example:

Suppose newspaper reporters brainstormed some headlines for an article on this experiment.  These are their suggested headlines:

A. “New Treatment Helps Pericarditis Patients”
B. “Colchicine Tends to Improve Treatment for Pericarditis”
C. “Pericarditis Patients May Get Help”

7. Which of the headlines above would be best to use for the article?  Explain why.

Headline A would be the best because this is a well-designed experiment.  Therefore, a cause and effect relationship has been established.  Headlines B and C talk about a tendency relationship, not a cause and effect relationship.

“Tends to improve” implies a causal relationship, as does “help” in this context. The authors appear to have confused “causal” with “deterministic.”

The quality issues we see associated with the implementation of Common Core bear a striking resemblance to the problems noted by Richard Feynman when critiquing the New Math reforms of the Sixties.

The reason was that the books were so lousy. They were false. They were hurried. They would try to be rigorous, but they would use examples (like automobiles in the street for "sets") which were almost OK, but in which there were always some subtleties. The definitions weren't accurate. Everything was a little bit ambiguous -- they weren't smart enough to understand what was meant by "rigor." They were faking it. They were teaching something they didn't understand, and which was, in fact, useless, at that time, for the child.