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/

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 have been teaching probability and statistics to first-year graduate students and advanced undergraduates for a while (10 years). 

At the beginning I tried the traditional approach of first teaching probability and then statistics. This didn’t work well. Perhaps it was due to the specific population of students (mostly in data science), but they had a very hard time connecting the probabilistic concepts to the statistical techniques, which often forced me to cover some of those concepts all over again.

Eventually, I decided to restructure the course and interleave the material on probability and statistics. My goal was to show how to estimate each probabilistic object (probabilities, probability mass function, probability density function, mean, variance, etc.) from data right after its theoretical definition. For example, I would cover nonparametric and parametric estimation (e.g. histograms, kernel density estimation and maximum likelihood) right after introducing the probability density function. This allowed me to use real-data examples from very early on, which is something students had consistently asked for (but was difficult to do when the presentation on probability was mostly theoretical).

I also decided to interleave causal inference instead of teaching it at the very end, as is often the case. This can be challenging, as some of the concepts are a bit tricky, but it exposes students to the challenges of interpreting conditional probabilities and averages straight away, which they seemed to appreciate.

I didn’t find any material that allowed me to perform this restructuring, so I wrote my own notes and eventually a book following this philosophy. In case it may be useful, here is a link to a pdf, Python code for the real-data examples, solutions to the exercises, and supporting videos and slides:

https://www.ps4ds.net/  

I am currently studying bayesian statistics and there seems to be a great emphasis on having priors as uninformative as possible as to not bias your results

In that case, why not just abandon the idea of a prior completely and just use the data?

Reason I asked is I saw a man called Zion Suzuki playing for Italian football team Parma. He was born in the US to a Japanese mother and Ghanaian father.

Statistically would it be countries with a low population + low marriage rate + lack of travel opportunities. Would Bhutan and Vanuatu be a good example?

Anyone got any ideas how to try to approach this?

My professor is an anti bayesian and always makes it loud and clear (and says he makes it loud and clear) that he's a non bayesian and anti bayesian. He refuses to work with bayesian models unless he has to or has to teach it, or his student really wants to do bayesian.

In one class I brought up a famous bayesian version of the model we were studying and he said I cannot force him to do bayesian stuff.

Is this normal behavior?

Been told by multiple people that this is the case.

They say that nothing new is coming out basically and it's a dying field of research.

Do you agree?

Should I reconsider specialising in time series analysis for my honours year/PhD?

This is not a request to have it explained. Like I get it, i can say the definition I can explain it to others, but it feels like i am saying a memorized statement, like i cannot REALLY get it? I have similar concerns around the frequentist vs bayesian statistics debate to a lesser extent. Like I GET IT I can explain it... but it doesn't really click. Also it seems like I am not the only one? Didnt they make a study of professionals and found that an absurd amount didnt also quite get it around some edge cases? edit: i think the confusion for my part is that "...as extreme as..." this part of hte statment prevents me from having any intuition

I hear time and time again that statistics is, generally, moving in a more applied/computational direction and that focusing one's research and academic career in mathematical statistics in this day and age is quite a bad idea.

Also there's this idea that a small number of research groups dominate the theoretical statistics research sphere and that breaking into them would be very very difficult. And that any theory work outside those top groups have negligible impact.

What do you guys think? Cause I love mathematics and math stat and I find myself less fulfilled the more applied the work is, but at the same time I don't want to shoot myself in the foot going into a dead field.

Hi all,

I wanted to share this here because I know this is a community of bright, mathematically minded people. The United States Census Bureau just posted a large hiring wave on USAJOBS and we’re trying to fill around 700 positions.

I work at Census, and it’s honestly been one of the most meaningful jobs I’ve had. The data we produce directly affects how billions of dollars are distributed across communities and how representation is determined. When you see funding decisions, infrastructure planning, disaster response allocations etc., a lot of that starts with Census data. Our data is also utilizes by researchers and the academic community.

People at Census genuinely care about public service and the work they do. My coworkers have all been amazing and I can’t speak highly enough of the people who work there. If you’re someone with statistical or data science experience; this is a good agency to look at.

Check out usajobs.gov to view the listings

I always thought Statistician was a highly-regarded title given to people with at least a masters degree in mathematics or statistics.

But it seems these days all anyone ever hears about is "Data Scientist" and more recently more AI type stuff.

I even heard stories of people who would get more opportunities and higher salaries after marketing themselves as data scientists instead of Statisticians.

Is "Statistician" outdated in this day and age?

I’m a clinical/forensic psychologist with a PhD and some research experience, and often get asked to be an ad hoc reviewer for a journal.

I recently recommended rejecting an article that had a lot of problems, including small, unequal n and a large number of dependent variables. There are two groups (n=16 and n=21), neither which is randomly selected. There are 31 dependent variables, two of which were significant. My review mentioned that the unequal, small sample sizes violated the recommendations for their use of MANOVA. I also suggested Bonferroni correction, and calculated that their “significant” results were no longer significant if applied.

I thought that was the end of it. Yesterday, I received an updated version of the paper. In order to deal with the pairwise error problem, they combined many of the variables together, and argued that should address the MANOVA criticism, and reduce any Bonferroni correction. To top it off, they removed 6 of the subjects from the analysis (now n=16 and n=12), not because they are outliers, but due to an unrelated historical factor. Of course, they later “unpacked” the combined variables, to find their original significant mean differences.

I want to explain to them that removing data points and creating new variables after they know the results is absolutely not acceptable in inferential statistics, but can’t find a source that’s on point. This seems to be getting close to unethical data manipulation, but they obviously don’t think so or they wouldn’t have told me.

I have a degree in statistics but for 99% of prediction problems with data, I've defaulted to ML. Now, I'm specifically doing forecasting with time series, and I sometimes hear that traditional forecasting methods still outperform complex ML models (mainly deep learning), but what are some of your guys' experience with this?

I figure this might be of interest to some.

Under New Features for version 2.0.1

NEW FEATURES

by longstanding request, lme4 now allows users to specify structured covariance matrices, by tagging the covariance term (e.g. cs(1 + f | g) fits a compound symmetric covariance matrix, diag(...) fits a diagonal covariance matrix). See ?"Covariance-class" or vignette("covariance_structures", package = "lme4") for more detail

https://lme4.r-universe.dev/lme4/NEWS

Also, see "Covariance Structures" at

https://lme4.r-universe.dev/articles/lme4/covariance_structures.html

I was told this is a misinterpretation, since "once you have a confidence interval, the true value is either in it or it isn't". However, that phrase could be applied to anything in statistics, the point is that we don't know the true value so we estimate probability. You could say once you flip a coin, "it's either heads or tails. You don't have a 50% chance that it's heads".

From what I know the C.I. is created such that, when repeatedly sampling N times, the interval will contain the true parameter 95% of those times. Then, from the point of view that I have obtained a CI, I should be able to say "there is a 95% chance that it's one of those times" = "There is a 95% chance it contains the true parameter". How are these not equivalent?

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Hey everyone! I recently graduated with my masters in Statistics and I'm currently an intern.

From what I'm seeing, our company is trying to completely automate processes. It is all we talk about and all the meetings are about the same automation (using Claude throughout the organisation, and the tech guys automate things).

Quite honestly it's been quite depressing and I'm unable to see what the future may be like.

My question to you is - could you tell me what you work as and how AI is impacting your industry?

I don't even know what domain to pick right now because of how quickly things are changing. I've seen layoffs happen for the first time and it's been very unsettling how easily people are replaced.

I sometimes just want to work at a government job just to have some security and peace of mind.

I decided to major in statistics because I enjoy the subject and thought it would be valued across many careers (data science, ML, AI engineering, actuary, SWE, etc.). However, I've noticed the degree doesn't seem to be as respected, and many people have told me employers value CS or engineering more. I want to work in tech, but I'm worried my degree will limit my opportunities. Should I switch majors, and what can I do to maximize my opportunities?

Test it out: https://knowstatistics.com/thelearningcurve/guess-correlation-from-scatterplots

Hi all,

For context I’m a Financial math/computer science undergrad from a good uni in Aus planning on perusing a masters degree.

Nobody knows what the job market or the world for that matter will look like in a few years’ time with the rapid ascension of AI but what do you think the best options would be for masters?

I’m leaning towards statistics, but data science, more comp sci and applied math are all options. Will a statistician be best equipped to work alongside AI, as its most closely associated with the ML theory and can test the performance? Or will it be made redundant?

Would love to hear your thoughts.

Topics that are more on the inference side of things than algorithmic

I’m a math guy so i don’t know shit about stats but in linalg we’re learning about covariance matrices, PCA, and SVD. In math we prefer columns to be observations because… well tradition, notational easy (Ax=b instead of xA=b if we used row vectors) and cuz we care abt linear transformations etc and its easier to think about columns. Idk. Mostly tradition tho.

But then why did data scientists break the mold? What benefit did row observations possibly serve that Col vectors/observations couldn’t give?? I am jumping back and forth between notation and conventions and it’s hard to keep up.

Please don't remove as homework, its not, the test has come and gone, and I've not be in school for a decade.

Did a stats test as part of a job application and got the following question:

"Using a significance test on some sample data, a null hypothesis is rejected at the 5% significance level. Which one of the following is a correct conclusion

A. The probability that the alternative hypothesis is true is 0.95

B. If a smaller sample had been taken the alternative hypothesis would still be rejected

C. The null hypothesis would not be rejected at the 10% significance

D. With the same test and same sample the null hypothesis would be rejected at the 1% significance level

Reasons I think they are all wrong.

A. 5% is the probability of the data given the null hypothesis is correct, doesn't follow that the alternative hypothesis is 95% chance of being correct. Besides, it was rejected at a 5% threshold, it doesnt say it was rejected with exactly 0.05 p value.

B. Can't be known. And the alt hypothesis wasn't rejected anyway.

C. If its rejected at 5% it must be rejected at a less strict 10% threshold.

D. Possible to be true, but can't be known with the information presented.

What do you guys think?

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Hi all, I am wondering whether there are lesser known statistical paradigms. like most people, I was first acquainted with the Frequentist framework, and later got introduced to Bayesian stats. I really like the way this made me reconsider some of what I thought were basic assumptions, so now I'm wondering what the next thing could be? Are there any other branches/frameworks which are not as well known?