Learning New Methods: Just-in-Time or Just-in-Case

January 21, 2013

(This article was originally published at Carlisle Rainey » Methods/Statistics, and syndicated at StatsBlogs.)

Matt Dickenson has an interesting new post up at his blog that discuss strategies for learning new skills.

You will never be dumber than you are right now. You will also never have more time than you do right now. Thus, you have a relative abundance of time and a relative dearth of knowledge. How do we strike a balance between these resources to optimally leverage them for learning?

He argues that, in some instances, we pick up new skills at the point of need. Other times, we learn skills well beforehand in anticipation of need. Matt thinks that just-in-time learning is undervalued.

To answer the question we started with, I think that we need to place more value on just-in-time learning and less on just-in-case learning.

I tend to agree, but the two are not independent. For example, just-in-case training in probability theory allows more efficient just-in-time learning of specific statistical models.

I think this has some application to how we train graduate students in political methodology.

I was taught the details of various statistical models well before I needed them. For example, I learned a lot about duration models, although I've never applied beyond the practice sets. I've also watched Gary King's lectures and he structures his class similarly.

I think political scientists should consider an alternative strategy for educating graduate students. Rather than teach students the details of many statistical models, we could focus more on probability theory. Not probability theory at the expense of specific models, but probability theory in addition to specific models. With a stronger background in probability theory, students can better understand models they already know, explain their inferences to others, and efficiency learn or derive new statistical models just-in-time.

When I took "maximum likelihood," for example, the class spent one day studying MLE and then moved on to specific applications (e.g. logit and probit). That balance doesn't seem quite right to me.

I think political scientists could give up a little breadth in the number of models that students learn and gain some depth in the foundations of probability theory. This sacrifices some just-in-case learning of statistical models for just-in-case learning of probability theory. Which would help students more?

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