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

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.

My brain revolts when I’m trying to learn advanced math, even though I generally have no problem with abstract concepts. Please help.

Edit to add: thank you so much for all the great explanations!!!! I cannot reply to them all, but y’all have helped me greatly with actually understanding these data sets in my homework, and I am grateful 🩷

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?

Although I feel like a dummy when going back to relearn the basics, I do gain a different and deeper understanding of the formulas. Is it always going to be like this? Do you guys deep into your career still go back to the basics and deepen your understanding? Is it still like this 10 years later? Its my first time studying in uni and I dont like the idea that I have to sometimes just accept that I wont have full clarity over the mathematical tools im using, and that only later on the tools gain clarity. Sorry for any misspellings english is my third language.

From my understanding Marylin Savants explanation is as follows; When you first pick a door, there is a 1/3 chance you chose the car. Then the host (who knows where the car is) always opens a different door that has a goat and always offers you the chance to switch. Since the host will never reveal the car, his action is not random, it is giving you information. Therefore, your original door still has only a 1/3 chance of being right, but the entire 2/3 probability from the two unchosen doors is now concentrated onto the single remaining unopened door. So by switching, you are effectively choosing the option that held a 2/3 probability all along, which is why switching wins twice as often as staying.

Clearly switching increases the odds of winning. The issue I have with this reasoning is in her claim that’s the host is somehow “revealing information” and that this is what produces the 2/3 odds. That seems absurd to me. The host is constrained to always present a goat, therefore his actions are uninformative.

Consider a simpler version: suppose you were allowed to pick two doors from the start, and if either contains the car, you win. Everyone would agree that’s a 2/3 chance of winning. Now compare this to the standard Monty Hall game: you first pick one door (1/3), then the host unexpectedly allows you to switch. If you switch, you are effectively choosing the other two doors. So of course the odds become 2/3, but not because the host gave new information. The odds increase simply because you are now selecting two doors instead of one, just in two steps/instances instead of one as shown in the simpler version.

The only way the hosts action could be informative is if he presented you with car upon it being your first pick. In that case, if you were presented with a goat, you would know that you had not picked the car and had definitively picked a goat, and by switching you would have a 100% chance of winning.

C.! → (G → G)

G. → (C! → G)

G. → (G → C!)

Looking at this simply, the hosts actions are irrelevant as he is constrained to present a goat regardless of your first choice. The 2/3 odds are simply a matter of choosing two rather than one, regardless of how or why you selected those two.

It seems Savant is hyper-fixating on the host’s behavior in a similar way to those who wrongly argue 50/50 by subtracting the first choice. Her answer (2/3) is correct, but her explanation feels overwrought and unnecessarily complicated.

My homework is graded and done. So I hope this flies. Sorry if it doesn't.

Genetics class. My understanding (grinding through like 5 sources) is that p-value x 100 = the % chance your results would be obtained by random chance alone, no correlation , whatever (null hypothesis). So a p-value below 0.05 would be a <5% chance those results would occur. Therefore, null hypothesis is less likely? I got a p-value on my Mendel plant observation of ~0.1, so I said I needed to reject my hypothesis about inheritance, (being that there would be a certain ratio of plant colors).

Yes??

I wrote in the margins to clarify, because I was struggling: "0.1 = Mendel was less correct 0.05 = OK 0.025 = Mendel was more correct"

(I know it's not worded in the most accurate scientific wording, but go with me.)

Prof put large X's over my "less correct" and "more correct," and by my insecure notation of "Did I get this right?" they wrote "No." They also wrote that my plant count hypothesis was supported with a ~0.1 p-value. (10%?) I said "My p-value was greater than 0.05" and they circled that and wrote next to it, "= support."

After handing back our homework, they announced to the class that a lot of people got the p-values backwards and doubled down on what they wrote on my paper. That a big p-value was "better," if you'll forgive the term.

Am I nuts?!

I don't want to be a dick. But I think they are the one who has it backwards?

Edit: I understand why I am wrong now.

The game is as follows:

- There are 3 doors with prizes, 2 with goats and 1 with a car.

- players picks 1 of the doors.

- Regardless of the door picked the host will reveal a goat leaving two doors.

- The player may change their door if they wish.

Many people believe that since pick 1 has a 2/3 chance of being a goat then 2 out of every 3 games changing your 1st pick is favorable in order to get the car... resulting in wins 66.6% of the time. Inversely if you don’t change your mind there is only a 33.3% chance you will win. If you tested this out a 10 times it is true that you will be extremely likely to win more than 33.3% of the time by changing your mind, confirming the calculation. However this is all a mistake caused by being mislead, confusion, confirmation bias, and typical sample sizes being too small... At least that is my argument.

I will list every possible scenario for the game:

1. pick goat A, goat B removed, don’t change mind, lose.
2. pick goat A, goat B removed, change mind, win.
3. pick goat B, goat A removed, don’t change mind, lose.
4. pick goat B, goat A removed, change mind, win.
5. pick car, goat B removed, change mind, lose.
6. pick car, goat B removed, don’t change mind, win.

I am performing an ordinal regression analysis with 10 predictors, reduced from an original set of 50. Sample size: N = 380. One of these predictors is the primary predictor of interest (Safety Hazards [SH], a continuous variable). The rest are potential confounders of the primary predictor or contributors to the model.

My primary outcome is Job Satisfaction (JS).

I distilled the 10 candidates from the original 50 by running ordinal bivariate regressions on each candidate.

a) A crude model [JS <-- (beta)*SH]

b) 49 adjusted models: [JS <-- (beta)*SH + any of the 49 predictors].

1. If any of the bivariate regressions produced a CIE of 10% or more for the primary predictor, SH, it made the list of 10, regardless of its p- value.
2. If any of the 50 candidates had a p- value of < = 0.1, regardless of its CIE value, it made it to the list of 10 as well.

I sort the list of 10 by CIE value and use that order to determine which variable to test next in the model.

I then begin the final fitting process to extract the most plausible confounders of SH and true model contributors from the 10 predictors. I use 2 different regression methods: Forward Stepwise Regression (FSR) and Backward Stepwise Regression (BSR).

The conditions to stay in the model are: "If the p-value > 0.1 and CIE <10%, then reject from the model; otherwise, retain."

In FSR, I start with a sparse (crude) model, sequentially add a predictor, and test whether it meets the retention criteria. If so, I retain it, add the next predictor to the new model, and apply the same retention/rejection criteria until all predictors have been tested.

In BSR, I start with a full model and sequentially remove predictors, one at a time, and perform similar rejection/retention tests as in FSR until all predictors have been tested.

However, I find that the FSR and BSR algorithms retain and reject different variables. To be clear, about 60% of variables are common to both algorithms.

My question is: why do the FSR and BSR algorithms produce different results?

What is different about starting with a full model (BSR) versus a sparse model (FSR)?

What is different about starting with a sparse model (FSR) versus a full model (BSR)?

Please try to give an explanation that does not involve the term 'variance', as I tried to explain to my PI, only to be told that is the wrong way to explain it.

Thanks

David

​

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?

Tony Gonzales (love him), pointed out that teams that start out 0-3 have a 2.9% chance of making the playoffs (when talking about the Texans). He then asked what are the chances of doing it two years in a row, which he pointed out is 0.08%. That's correct (0.029)^2. He went on to say because of the even lower odds, i.e. 0.08%, he didn't think they'd make the playoffs this year. But last year's results are irrelevant now. Their chances of making the playoffs this year with an 0-3 record are still 2.9%. Am I wrong?

Lets say you open a door 30 times, and 0 times theres a ghost. Someone then asks: whats the probability there is a ghost behind the door 10% of the time?

First I would correct the question and say “I’m assuming you mean at LEAST 10% of the time, since the probability of exactly 10% of the time is near 0“

Next, I would do (1-0.1)^30 (the probability you dont see the ghost 30 times in a row if the true probability is 10%) = 4%, in other words, theres a 96% chance wed have seen at least one ghost if the true probability was 10%. Im tempted to say that also means theres a 96% chance the rate of seeing a ghost is not in fact 10% or more, but technically can that ONLY be answered with bayesian stats? if so what is the calculation (in R)

All analysis on pre-Election polls implicitly assumes that, if they are accurate, they will predict the election result and/or the margin.

That's not true.

It's a truth as simple as the Margin of Error formula itself.

If a poll says that 10% of voters are undecided, their eventual preference cannot be assumed - unconditional probability cannot be assumed. There is no logical, philosophical, or mathematical rule that says undecideds can't favor the candidate behind.

Yet that simple fact violates the analysis done on poll data worldwide.

Is this worth fixing or is it not important?

Edit: since the first comments on this post appear to have intentionally or unintentionally misunderstood my point, let me be very specific:

Given a pre-election poll or poll average that states

Candidate A: 46% Candidate B: 44% Undecided: 10%

And an election of: Candidate A: 52% Candidate B: 48%

How much error did that poll have?

I hear a lot of angst about the role of statisticians (especially junior statisticians) with the rise of AI. Having worked as faculty for a good bit; run an MS program; and engaged with various companies, I had a few thoughts that I think are maybe useful for junior folks (though maybe speculative). I don't have answers (and obviously cannot predict the future), but I think there has historically been a myth that is worth explicitly clearing up. The myth has several versions:

-----------------------

The role of a statistician is to compute statistics.

OR

The role of a statistician is to select statistical models, apply them to data, and interpret their outputs

OR even

The role of a statistician is to analyze data.

-----------------------

The first is obviously a bit silly, but I think they all have the same issue: they are much too small in scope, and limit people individually, as well as limiting us as a field.

In my mind, the primary role of a statistician is to identify and engage with challenging real world problems that have uncertainty; to identify how data can be used to qualitatively or quantitatively interrogate that uncertainty; and then, [generally using data,] to make qualitative and/or quantitative statements that support action (ideally) and/or understanding.

Maybe, even more broadly, to use that style of thinking to creatively add value to whatever organization they are part of.

The above says nothing about the use of statistical models, statistical software, or even quantitative data analysis.

Graduate programs often do a terrible job teaching this (for more reasons than I can cover here). And this work often requires a TON of "soft" skills (that are often, at best, tokenized)

Ok, you might say, how do I do this as a junior statistician?? Often this means asking a ton of broad questions, and independently learning a lot (eg. if you are at a biostat CRO, or pharma company, maybe learning deeply about the diseases and medications you are engaging with, about regulations, about reimbursement, about the whole clinical trial pipeline; or the theory of group sequential trials. eg. In finance maybe it means learning deeply about the markets/financial-instruments you are trading, related regulations, quirks of the data, etc). Beyond all that, it means thinking deeply and creatively about the challenges of your organization. There's also, often, not a simple and obvious career path here (though, the high level managers/c-suite I talk to generally bemoan that they have way too few quantitively-minded people who can engage nimbly and holistically). If this sounds daunting, it's a marathon not a sprint, a lifetime of work -- and it should be fun! (though that's easier to say/feel when I'm not struggling to get my first position, out of grad school, I know). It is just not mechanical...

Some parts of the job of statisticians will likely be eaten by AI. However, in my experience, unless AI gets qualitatively much better, those will be the less creative/more-mechanical parts (though parts that do currently require skill!). If you see those parts as your whole job/career, then, I think, you are potentially in trouble. If you are instead focused on figuring out how to broadly and creatively support the mission of the groups/organizations you are part of, then I think there is much less existential threat. All that said -- lots of organizations absolutely suck (and the world is a bit of a mess), and I don't want to pretend that things won't be tumultuous in the short run.

I guess, in my mind, computers have always been good at "in-sample" tasks. Advances (eg. compilers, interpreters, various frameworks, etc...) have, over time, increased the scope of what "in-sample" looks like. AI has just vastly and asymmetrically increased that "in-sample" scope in ways that feel very unintuitive (claude "knows" every popular programming package and library, as well as all the methods/theory papers published in the last 200 years, in my experience, often struggles with simple and intuitive problem-solving in poorly documented areas), but there is actually still a lot of out-of-sample stuff (and, honestly, that out-of-sample stuff is always where statisticians were adding the most value). Maybe that gap will close soon, but it doesn't feel like it to me. That said, the gap is not in applying or interpreting more and more complex models.

As for graduate programs (and undergrad programs) -- I think there is a real reckoning coming here. I think there is still a real role for graduate programs training/mentoring students. But it has to be holistic and about helping students meaningfully learn to engage with out-of-sample tasks.

Thank you for coming to my uninvited TED talk. I'll see myself out.

In my machine learning class (in the computer science department) my professor said that a p-value of .05 would mean you can be 95% confident in rejecting the null. Having taken some stats classes and knowing this is wrong, I brought this up to him after class. He acknowledged that my definition (that a p-value is the probability of seeing a difference this big or bigger assuming the null to be true) was correct. However, he justified his explanation by saying that in practice his explanation was more useful.

Given that this was a computer science class and not a stats class I see where he was coming from. He also prefaced this part of the lecture by acknowledging that we should challenge him on stats stuff if he got any of it wrong as its been a long time since he took a stats class.

Instinctively, I don't like the idea of teaching something wrong. I'm familiar with the concept of a lie-to-children and think it can be a valid and useful way of teaching things. However, I would have preferred if my professor had been more upfront about how he was over simplifying things.

That being said, I couldn't think of any strong reasons about why lying about this would cause harm. The subtlety of what a p-value actually represents seems somewhat technical and not necessarily useful to a computer scientist or non-statistician.

So, is there any harm in believing that a p-value tells you directly how confident you can be in your results? Are there any particular situations where this might cause someone to do science wrong or say draw the wrong conclusion about whether a given machine learning model is better than another?

​

Edit:

I feel like some responses aren't totally responding to what I asked (or at least what I intended to ask). I know that this interpretation of p-values is completely wrong. But what harm does it cause?

Say you're only concerned about deciding which of two models is better. You've run some tests and model 1 does better than model 2. The p-value is low so you conclude that model 1 is indeed better than model 2.

It doesn't really matter too much to you what exactly a p-value represents. You've been told that a low p-value means that you can trust that your results probably weren't due to random chance.

Is there a scenario where interpreting the p-value correctly would result in not being able to conclude that model 1 was the best?

I saw this analogy made comparing AI finding proofs to open mathematical conjectures with AI constructing a model that finds a compound for treating cancer and statistically predicting that it would pass a Phase III clinical trial.

This seems like a problematic comparison to me because finding a proof to a conjecture is not the same as making a prediction with a statistical model. No matter how good a prediction is on paper, it is still no reason to treat that compound as though it had already passed Phase III (or even Phase I). On the other hand, a proof to a conjecture can be directly checked if it is right or wrong; there is no uncertainty to it. If the proof compiles on Lean, then it means that it is correct in as far as what being correct from compiling on Lean means.

Of course, an AI finding a compound that it predicts will pass a Phase III trial is still informative. It can motivate at least checking if the compound works invitro and if that checks out, one might be more motivated to proceed to a Phase I trial, but you would always still have to do the experiment, right?

Does this make sense or am I missing something?

Hi folks,

I'm applying to PhD programs this fall, and I'm currently looking to replace my old laptop. I'm not quite sure what to look for, but I'm inclined to think that a business-grade laptop might be worthwhile since they're built to handle daily wear-and-tear, and I'd like something that I can keep for 5-6 years. I've been looking at new ThinkPad and Latitude models, but they seem really expensive (especially for those with 32GB RAM).

Anyone who's currently a PhD student, which laptop do you have, and what are its specs? What should I look for if I want to hold onto the laptop for the duration of my program?

For info, I am a PhD student. My stipend is 12,500 a year and I have to pay for this shit myself. Please let me know if I am being irrational.

Two years ago, I purchased access to a 4-year student version of MPlus. One year ago, my laptop which had the software on it died. I got a new laptop and went to the Muthen & Muthen website to log-in and re-download my software. I went to my completed purchases tab and clicked on my license to download it, and was met with a message that my "Update and Support License" had expired. I wasn't trying to update anything, I was only trying to download what i already purchased but okay. I contacted customer service and they fed me some bullshit about how they "don't keep old versions of MPlus" and that I should have backed up the installer because that is the only way to regain access if you lose it. I find it hard to believe that a company doesn't have an archive of old versions, especially RECENT old versions, and again- why wouldn't that just be easily accessible from my account? Because they want my money, that's why. Okay, so now I don't have MPlus and refuse to buy it again as long as I can help it.

Now today I am having issues with SPSS. I recently got a desktop computer and looked to see if my license could be downloaded on multiple computers. Apparently it can be used on two computers- sweet! So I went to my email and found the receipt from the IBM-selected vendor that I had to purchased from. Apparently, my access to my download key was only valid for 2 weeks. I could have paid $6.00 at the time to maintain access to the download key for 2 years, but since I didn't do that, I now have to pay a $15.00 "retrieval fee" for their customer support to get it for me. Yes, this stuff was all laid out in the email when I purchased so yes, I should have prepared for this, and yes, it's not that expensive to recover it now (especially compared to buying the entire product again like MPlus wanted me to do) but come on. This is just another way for companies to nickel and dime us.

Is it just me or is this ridiculous? How are people okay with this??

EDIT: I was looking back at my emails with Muthen & Muthen and forgot about this gem! When I had added my "Update & Support" license renewal to my cart, a late fee and prorated months were included for some reason, making my total $331.28. But if I bought a brand new license it would have been $195.00. Can't help but wonder if that is another intentional money grab.

For people currently in grad school or working in the field which do you find more useful/relevant right now, for coursework and for the job market afterward?

Hopefully something that is not AI related.

I keep hearing people saying measure theory or some sort of “mathematical maturity” is important when trying to get a genuine understanding of probability and more advanced statistics like stochastic calculus.

What’s your opinion? If you wanted to be the best statistician possible would you do a mathematical statistics, applied statistics, pure maths, applied maths or computer science major? What would be the perfect double major out of of those if possible.

[Discussion]

As the title says.

Hello everyone, I am a 2nd year neurosurgery resident in India.
I wanted to learn SPSS : the statistical software so that I can conduct my own statistics for the data I have collected for my research.
I have seen many videos floating online and wanted your advice regarding which one would be best for me to start with.

I do have a basic knowledge of statistics ( whatever was taught in medical school ) , but not more than that

Any suggestions are appreciated !
( also sorry if this is the wrong sub for this, please guide me to the correct one )

The prior is rarely if ever what anyone actually believes, and calling the posterior of "P(H|E) = P(E|H) * P(H) / P(E)" a belief update is confusing and misleading. All it does is narrow down the possibilities in one specific situation without telling us anything about any similar situations. I've been searching for explanations of where the belief-update language came from. I have some ideas, but I'm not really sure about them. One is that when some philosophers in the line of Ramsey were looking for an asynchronous rule, they misunderstood what the formula does, from wishful thinking and lack of statistical training. Or maybe even Jeffreys himself misrepresented it. Another possibility I see is that when a parameter probability distribution is updated by adding counts to pseudo-counts, the original distribution is called "prior" and the new one is called "posterior," the same words used for the formula, and sometimes even trained statisticians call that "Bayesian updating" and "updating beliefs." Maybe people see that and think that it's using the formula, so they think that the formula is a way of updating beliefs.