Fixing the reproducibility crisis: Openness, Increasing sample size, and Preregistration ARE NOT ENUF!!!!

April 15, 2018

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

In a generally reasonable and thoughtful post, “Yes, Your Field Does Need to Worry About Replicability,” Rich Lucas writes:

One of the most exciting things to happen during the years-long debate about the replicability of psychological research is the shift in focus from providing evidence that there is a problem to developing concrete plans for solving those problems. . . . I’m hopeful and optimistic that future investigations into the replicability of findings in our field will show improvement over time.

Of course, many of the solutions that have been proposed come with some cost: Increasing standards of evidence requires larger sample sizes; sharing data and materials requires extra effort on the part of the researcher; requiring replications shifts resources that could otherwise be used to make new discoveries. . . .

This is all fine, but, BUT, honesty and transparency are not enough! Even honesty, transparency, replication, and large sample size are not enough. You also need good measurement, and some sort of good theory. Otherwise you’re just moving around desk chairs on the . . . OK, you know where I’m heading here.

Don’t get me wrong. Sharing data and materials is a good idea in any case; replication of some sort is central to just about all of science, and larger sample sizes are fine too. But if you’re not studying a stable phenomenon that you’re measuring well, then forget about it: all those good steps of openness, replication, and sample size will just be expensive ways of learning that your research is no good.

I’ve been saying this for awhile so I know this is getting repetitive. See, for example, this post from yesterday, or this journal article from a few months back.

But I feel like I need to keep on screaming about this issue, given that well-intentioned and thoughtful researchers still seem to be missing it. I really really really don’t want people going around thinking that, if they increase their sample size and keep open data and preregister, that they’ll solve their replications. Eventually, sure, enough of this and they’ll be so demoralized that maybe they’ll be motivated to improve their measurements. But why wait? I recommend following the recommendations in section 3 of this paper right away.

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