I'm an independent scholar of (that's just starting out on the indie scholar path). I'm interested in microeconomic theory, , , , and .

I am still in the process of figuring out precisely what I want to study, but I'm fascinated by economic theory — the models and the process of developing models and critiquing and applying them.

I've really been enjoying these two papers since watching a video presentation about the first one:

1. Shmueli, G., "To Explain or To Predict?", Statistical Science, vol. 25, issue 3, pp. 289-310, 2010.
- Video presentation:

2. Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", MIS Quarterly, vol. 35, issue 3, pp. 553-572, 2011.

PDFs available here:

I've been a lot more chill over the past few weeks. I'd been stressing for a long while about my career and wanting to get to a point where I can professionally work on or stuff that interest me, but I think I'll just do that in my free time for now. (As an independent scholar.)

My current job is fine for me, seems secure, and is quite complimentary to my interests, so I'll just find fulfillment on my own time instead of struggling to force it into my career. 😅

I've been reading PDFs of on my e-reader, which is only grayscale. Lots of figures are using color to let the reader differentiate between distributions and data points, which doesn't help me at all due to the grayscaling. But I'm kinda more amused by the process of inferring which things are which than I should be. 😂

It'd be kinda cool if a statistical programming language / package provided checks on whether one's use of methods is theoretically proper or not.

Recently I managed to realize that I was planning to plot some of my personal finance data in a way that would suggest that path dependence isn't playing an important role in it, when it quite likely is.

Likely wrong 

I'm not sure, but given how I recently heard p-values explained, I think that if I know the p value required (I might have them mixed up, .9 or .1), the required outcome of a draw ($0.02), and the size of the distribution (number of seconds in a day, my unit), I can estimate this. It'd be [correct p value]*[seconds in a day]*0.02

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Fans of , I have a fun problem / for you (I've been trying it): suppose that you want to rise to the level of receiving a passive income such that you can honestly suggest that you earn pennies (>1) while you sleep. With a p of 0.1, how many pennies might you need to earn within a 24 period? I imagine that there's different ways of solving this.

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