e10v_me

9 ms·

1.
by e10v_me 5mo ago
> I think they'll only serve to create a barrier for junior developers. I proposed some ideas how to lower entry barriers: https://e10v.me/matching-markets-congestion/#entry-barriers-... > I'm a support
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Open source contribution and job search as matching markets under AI congestion(e10v.me)
3 points by e10v_me 5mo ago|4 comments
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by e10v_me 5mo ago
In one of my previous posts, I discussed congestion in the job market caused by the surge of AI tools that scrape job descriptions and auto-apply to jobs. Since that post, another problem has emerged: progress in the capabilities of coding
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by e10v_me 6mo ago
I'm surprised by how many people has fallen for that. I also wonder how many of them are the author's friends or bots.
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Comparing Python packages for A/B test analysis (with code examples)(e10v.me)
9 points by e10v_me 7mo ago|1 comments
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by e10v_me 7mo ago
I published a practical comparison of Python packages for A/B test analysis: tea-tasting, Pingouin, statsmodels, and SciPy. Instead of choosing a single "best" tool, I break down where each package fits and how much manual wo
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Prices as model weights: learning from dispersed data(e10v.me)
3 points by e10v_me 11mo ago|1 comments
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by e10v_me 11mo ago
I was thinking about the labor market congestion problem and came up with a solution that is often used in service marketplaces: pay to apply. Then I asked myself what this solution has that AI doesn't. That’s how I arrived at the anal
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by e10v_me 2y ago
Funny, the same) When I was switching from R (data.table) to Python, it was painful. Not only because it was slow, but because of the API. At that time, I thought that maybe it's because of switching to something new. Several years lat
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by e10v_me 2y ago
Congrats! What’s impressive is not just the speed of the tools Astral develops but also the speed of delivery. I wonder, if you plan to extend the functionality of building and publishing packages. For example, support for dynamic version (
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Debunking the Dunning–Kruger effect with random number simulation(e10v.me)
2 points by e10v_me 2y ago|1 comments
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by e10v_me 2y ago
Here’s another take on the Dunning–Kruger effect. I made two main points: 1. Consider N independent observations of two variables, X1 and X2, with imperfect correlation. Next, we assign percentiles to them: P1 and P2, respectively. We then
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by e10v_me 2y ago
Thank you for explanation and for drawing a vivid picture) I will add FWER and FDR to the roadmap. Which specific controlling procedures do you find the most useful on practice? I'm considering the following: - FWER: Holm–Bonferroni, H
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by e10v_me 2y ago
Thank you for explanation. If I understand correctly, you use this approach to increase sensitivity (compared to NHST) using the same data . Most probably, in your case, higher sensitivity (or power) comes at the cost of higher type I erro
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by e10v_me 2y ago
Thank you, I'm aware of this. But I don't understand how your link answers my previous message. I was asking for example of how to fit it using only aggregated statistics (focus on "aggregated"). I'm afraid the MCMC
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by e10v_me 2y ago
You would need probability distributions anyway. In Python, SciPy is the most mature and popular package with probability distributions. And it depends on NumPy. But I'll gladly consider better alternatives if you propose them.
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by e10v_me 2y ago
I thought about multiple comparison corrections. Here what my thoughts were: 1. Experiments with 3 or more variants are quite rare in my practice. I usually try to avoid them. 2. In my opinion, the Bonferroni correction is just wrong. It&#x
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by e10v_me 2y ago
Thank you! I hope it will be useful for you. Regarding your question, first, I'd like to understand what problem you want to solve, and whether this approach will be useful for other users of tea-tasting.
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by e10v_me 2y ago
Thank you. I will think about it. There are many different things I can add. The idea is to focus on the most needed features first. And the word "exotic" tells for itself ;)
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by e10v_me 2y ago
Not at the moment. If you have a specific method in mind, I will gladly look into it.
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by e10v_me 2y ago
> Most common statistical software (e.g. statsmodels) will support this grouped format. Interesting, I didn't know this about statsmodels. But maybe documentation a bit misleading: "A nobs x k array where nobs is the number of
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by e10v_me 2y ago
CUPED is easier than stratifying users. Or, probably, you mean post-stratification. Still, CUPED is easier, on my personal opinion :)
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by e10v_me 2y ago
I will consider it. Thank you for the suggestion.
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by e10v_me 2y ago
I cannot agree more. It's one of the reasons I've developed the package. With tea-tasting, I can save time and focus on the more important tasks, like experiment design.
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by e10v_me 2y ago
Thank you for the interest and for the suggestion. Yes, one can analyze A/B tests in a regression framework. In fact, CUPED is an equivalent to the linear regression with a single covariate. Would it be better? It depends on the defini
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by e10v_me 2y ago
Agree. I also suggest looking at Alex Deng's unfinished book on causal inference and, particularly, A/B testing: https://alexdeng.github.io/causal/ Alex Deng worked with Ron Kohavi at Microsoft Analysis and E
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by e10v_me 2y ago
Yeah, I know :) But it's in the roadmap. Btw, aren't Barnard's test or Boschloo's test better alternatives?
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Show HN: Tea-tasting, a Python package for the statistical analysis of A/B tests(e10v.me)
150 points by e10v_me 2y ago|48 comments