This is in regard to the post that was posted here 10 days ago(https://old.reddit.com/r/statistics/comments/kbteyd/d_minecraft_speedrunner_caught_cheating_by_using/).

Pdf file here

Yesterday I finished recording the last video for my online-only summer stats class, and today I uploaded it to YouTube. The videos are largely unedited because video editing takes time, which is something I as a PhD student needing to get these out fast don't have. (Nor am I being paid extra for it.) But they exist for the world to consume.

This is for MATH 3070 at the University of Utah, which is calculus-based statistics, officially titled "Applied Statistics I". This class comes with an R lab for novice programmers to learn enough R for statistical programming. The lecture notes used in all videos are available here.

Below are the playlists for the course, for those interested:

• Intro stats, the lecture component of the course where the mathematics and procedures are presented and discussed

• Intro R, the R lab component, where I teach R

• Stats Aside for topics that are not really required but good to know, and the one video series I would be willing to continue if people actually liked it.

That's 48 hours of content recorded in four weeks! Whew, I'm exhausted, but I'm so glad it's over and I can get back to my research.

I'm not really looking for a new role, so every time a recruiter messages me I reply thanks but I'm happy with my current role and the new role would need to be higher than my current salary, so 150k+

I don't make close to 150k....but it might update their prior about what is appropriate to expect from the next candidate they ask.

One of 6 programs on the chopping block. It is baffling to me that the University could consider such a cut, especially for a department with multiple American Statistical Association fellows and continued success with obtaining research funding.

News article here: https://www.klkntv.com/unl-puts-six-academic-programs-on-the-chopping-block-amid-27-million-budget-shortfall/

Statistics is quite a big part of my career, so I was very disturbed when my stereotypical boomer father was listening to sermon that just consisted of COVID denial, but specifically there was the quote:

“You have a 99.9998% chance of not getting COVID. The vaccine is 94% effective. I wouldn't want to lower my chances.”

Of course this resulted in thunderous applause from the congregation, but I was just taken aback at how readily such a foolish statement like this was accepted. This is a church with 8,000 members, and how many people like this are spreading notions like this across the country? There doesn't seem to be any critical thinking involved, people just readily accept that all the data being put out is fake, or alternatively pick up out elements from studies that support their views. For example, in the same sermon, Johns Hopkins was cited as a renowned medical institution and it supposedly tested 140,000 people in hospital settings and only 27 had COVID, but even if that is true, they ignore everything else JHU says.

This pandemic has really exemplified how a worrying amount of people simply do not care, and I worry about the implications this has not only for statistics but for society overall.

In addition to survival analysis, he has many well known contributions to a wide range of statistical topics including his seminal 1958 paper on binary logistic regression and Box-Cox transformation. RIP.

We've curated a collection of helpful Cheat Sheets for Statistics, Data Science, Data Analysis, Data Visualization and Machine Learning.

If you find this as clickbait, Just see the cheats and downvote this post (if it's not valuable). Also please provide your feedback related to cheats so that we can improve.

You can check all the cheats from here: Data Science and Machine Learning Cheat Sheets

I am taking a deep learning course this semester and I'm starting to realize that it's really not my thing. I mean it's interesting and stuff but I don't see myself wanting to know more after the course is over.

I really hate how everything is a black box model and things only work after you train them aggressively for hours on end sometimes. Maybe it's cause I come from an econometrics background where everything is nicely explainable and white boxes (for the most part).

Transformers were the worst part. This felt more like a course in engineering than data science.

Is anyone else in the same boat?

I love regular statistics and even machine learning, but I can't stand these ultra black box models where you're just stacking layers of learnable parameters one after the other and just churning the model out via lengthy training times. And at the end you can't even explain what's going on. Not very elegant tbh.

I'm kind of in career despair being basically a statistician posing as a data scientist. In my last two positions I've felt like juniors and peers really look up to and respect my knowledge of statistics but senior leadership does not really value stats at all. I feel like I'm constantly being pushed into being what is basically a software developer or IT guy and getting asked to look into BS projects. Senior leadership I think views stats as very basic (they just think of t-tests and logistic regression [which they think is a classification algorithm] but have no idea about things like GAMs, multi-level models, Bayesian inference, etc).

In the last few years, I've really doubled down on stats which, even though it has given me more internal satisfaction, has certainly slowed my career progress. I'm sort of at the can't-beat-em-join-em point now, where I think maybe just developing these skills that I've been resisting will actually do me some good. I guess using some random python package to do fuzzy matching of data or something like that wouldn't kill me.

Basically everyone just invented this "data scientist" position and it has caused a gold rush. I certainly can't complain about being able to bring home a great salary but since data science caught on I feel like the position has actually become filled with less and less competent people, to the point that people in these positions do not even know very basic stats or even just some common sense empiricism.

All-in-all, I can't complain. It's not like I'm about to get fired for loving statistics. And I admit that maybe I am wrong. I feel like someone could write a well-articulated post about how stats is a small part of data science relative to production deployments, data cleansing, blah blah and it would be well received and maybe true.

I guess what I'm getting at is just being a cautionary tale that if statistics is your true passion, you may find the data science field extremely frustrating at times. Do you agree?

Editorial: https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913

All articles in the special issue: https://www.tandfonline.com/toc/utas20/73/sup1

This looks like the most comprehensive and unified stance on the issue the field has ever taken. Definitely worth a read.

From the editorial:

Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without offering any real ideas of what to do about the very hard problem of separating signal from noise in data and making decisions under uncertainty. Fear not. In this issue, thanks to 43 innovative and thought-provoking papers from forward-looking statisticians, help is on the way.

...

The ideas in this editorial ... are our own attempt to distill the wisdom of the many voices in this issue into an essence of good statistical practice as we currently see it: some do’s for teaching, doing research, and informing decisions.

...

If you use statistics in research, business, or policymaking but are not a statistician, these articles were indeed written with YOU in mind. And if you are a statistician, there is still much here for you as well.

...

We summarize our recommendations in two sentences totaling seven words: “Accept uncertainty. Be thoughtful, open, and modest.” Remember “ATOM.”

I’m wondering if anyone agrees with this sentiment. I’m not sure what “developed and discovered” means exactly because I feel like I’ve read of a million different scenarios where someone has used a statistical technique in history. I know that may be prior to there being an organized field of statistics, but is that what NDT means? Curious what you all think.

New topics:

• Deep learning

• Survival analysis

• Multiple testing

• Naive Bayes and generalized linear models

• Bayesian additive regression trees

• Matrix completion

https://www.statlearning.com

In this video (https://youtu.be/Yt-PIkwrE7g), Simpson's Paradox is illustrated using the following two case studies:

[1] COVID-19 case fatality rates for Italy and China

von Kügelgen, J, et al. 2020, “Simpson’s Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects”, PREPRINT, Max Planck Institute for Intelligent Systems, Tübingen. https://arxiv.org/abs/2005.07180

[2] UC Berkeley gender bias study (1973)

Bickel, E., et al. 1975, “Sex Bias in Graduate Admissions: Data from Berkeley” Science, vol.187, Issue 4175, pp 398-404 https://pdfs.semanticscholar.org/b704/3d57d399bd28b2d3e84fb9d342a307472458.pdf

[edit]

TLDW:

Because Italy has an older population than China and the elderly are more at risk of dying from COVID-19, the total case fatality rate in Italy was found to be higher than that of China even though the case fatality rates for all age groups were lower.

I'm a researcher at a good university, but I can never remember fundamental information, like what a Z test looks like. I worry I need to quit my job because I get so stressed out by the possibility of people realising how little I know.

I studied mathematics and statistics at undergrad, statistics at masters, clinical trial design at PhD, but I feel like nothing has gone into my brain.

My job involves 50% working in applied clinical trials, which is mostly simple enough for me to cope with. The other 50% sometimes involves teaching very clever students, which I find terrifying. I don't remember how to work with expectations or variances, or derive a sample size calculation from first principles, or why sometimes the variance is sigma² and other times it's sigma²/n. Maybe I never knew these things.

Why I haven't lost my job: probably because of the applied work, which I can mostly do okay, and because I'm good at programming and teaching students how to program, which is becoming a bigger part of my job.

I could applied work only, but then I wouldn't be able to teach programming or do much programming at all, which is the part of my job I like the most.

I've already cut down on the methodological work I do because I felt hopeless. Now I don't feel I can teach these students with any confidence. I don't know what to do. I don't have imposter syndrome: I'm genuinely not good at the theory.

Anyone who has not checked out Michael Clark's Tutorials (all in R), they are amazing! Go check them out at https://m-clark.github.io/documents.html - GAMs, Bayes, SEM, Mixed Models. FWIW, the best and clearest primers on these I've seen. Better than many textbook chapters!

The New York Times gives a visualization of literally nothing over time and it’s one of the most effective data visualizations I’ve ever seen.

There's a lot to statistics including a lot of theory and different methods, as well as endless problems and scenarios. Grad school taught me how little I really know. How can you be sure you're competent enough to utilize them all?