I came across this post from 2016 which I still think is important.

The idea is that there should be some guidelines for experimental design, not just for “confirmatory” studies that produce estimates, standard errors, predictions, hypothesis tests, Bayesian inferences, etc., but also for exploratory research.

As Ed Hagen put it:

Exploratory studies need to become a “thing.” Right now, they play almost no formal role in social science, yet they are essential to good social science. That means we need to put as much effort in developing standards, procedures, and techniques for exploratory studies as we have for confirmatory studies. And we need academic norms that reward good exploratory studies so there is less incentive to disguise them as confirmatory.

Here’s what I wrote, a decade ago:

Suppose you know ahead of time that your theories are a bit vague and omnidirectional, that all sorts of interesting things might turn up that you will want to try to understand, and you want to move beyond the outmoded Psych Sci / PPNAS / Plos-One model of chasing p-values in a series of confirmatory studies.

You’ve thought it through and you want to do it right. You know it’s time for exploration first and confirmation later, if at all. So you want to design an exploratory study.

What principles do you have? What guidelines? If you look up “design” in statistics or methods textbooks, you’ll find a lot of power calculations, maybe something on bias and variance, and perhaps some advice on causal identification. All these topics are relevant to data exploration and hypothesis generation, but not directly so, as the output of the analysis is not an estimate or hypothesis test.

So I think we–the statistics profession–should be offering guidelines on the design of exploratory studies.

An analogy here is observational studies. Way back when, causal inference was considered to come from experiments. Observational studies were second best, and statistics textbooks didn’t give any advice on the design of observational studies. You were supposed to just take your observational data, feel bad that they didn’t come from experiments, and go from there. But then Cochran, and Rosenbaum, and Angrist and Pischke, wrote textbooks on observational studies, including advice on how to design them. We’re gonna be doing observational studies, so let’s do a good job at them, which includes thinking about how to plan them.

Same thing with exploratory studies. Data-based exploration and hypothesis generation are central to science. Statisticians should be involved in the design as well as the analysis of these studies.

So what advice should we give? What principles do we have for the design of exploratory studies?

Let’s try to start from scratch, rather than taking existing principles such as power, bias, and variance that derive from confirmatory statistics.

– Measurement. I think this has to be the #1 principle. Validity and reliability: that is, you’re measuring what you think you’re measuring, and you’re measuring it precisely. Related: within-subject designs or, to put it more generally, structured measurements. If you’re interested in studying people’s behavior, measure it over and over, ask people to keep diaries, etc. If you’re interested in improving education, measure lots of outcomes, try to figure out what people are actually learning. And so forth.

– Open-endedness. Measuring lots of different things. This goes naturally with exploration.

– Connections between quantitative and qualitative data. You can learn from those open-ended survey responses—but only if you look at them.

– Where possible, collect or construct continuous measurements. I’m thinking of this partly because graphical data analysis is an important part of just about any exploratory study. And it’s hard to graph data that are entirely discrete.

I think there’s room for something longer and more systematic on this topic. It’s important.