mattjack

Thanks for introducing me to these names, it is indeed a good place to start

Like I said this is personal preference. I've never used it but I think it makes culture worse so I hope it goes under.

I never know what people are going to like so maybe it will become the next TikTok instead, or someone will make a new Quibi that I also won't use or understand :)

A few others have mentioned that Quibi is not an interesting enough alternative timewasting app to Snapchat, Instagram, etc. I agree and I'm glad to see it fail for that reason; I want my attention span back and I'm trying to cut these things out of my life. I notice my well-being improves when I do.

That's personal preference obviously, and people are free to spend their time how they want, but I think the loss of attention span is a culture-scale problem now and excising these companies from our lives is a necessary treatment.

I hope Quibi folds permanently and we salt the earth where it stood.

It won't--so you'd have to use the backup methods they made you set up, like SMS codes, Authenticator, printable backup codes, etc.

They don't make this very obvious but this only works in Chrome. So you'll have to use SMS codes, the Authenticator app, or backup codes everywhere else. (edit: they explicitly say so when activating it but not as clearly in the docs)

Thank you so much for sharing this, I've been looking for something just like it.

I worded my comment incorrectly (and edited it accordingly). What I should have said is that when you run a stats test against a dataset, there's a known probability that you'll get a significant correlation simply due to chance. The more variables you examine, the higher that chance becomes.

I just found this on Google but the first page of this paper explains it a little better: http://www.stat.berkeley.edu/~mgoldman/Section0402.pdf

I agree with kharms

You're describing P-value hacking

Here's an example of what can happen when you take a huge corpus of data and throw an equally huge number of hypotheses at it to see what sticks: https://io9.gizmodo.com/i-fooled-millions-into-thinking-choc...

tl;dr: he "proved" chocolate causes weight loss by comparing chocolate- and non-chocolate-eaters on a very high number of health indicators.

That also introduces the multiple testing problem: https://www.wikiwand.com/en/Multiple_comparisons_problem

The more statistical tests you run against a set of data (EDIT: the more variables you test against a dataset), the higher the chance you get a statistically significant result from random error alone.