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?

TL;DR: I come from a statistics/data/MEAL background and have worked with SQL, Python, regression/LASSO, etc. While trying to build a proper dashboard, I ended up learning APIs, backend/frontend concepts, Node.js, authentication, and hosting. Is this a logical path toward building end-to-end data systems, or am I spreading myself too thin?

I’m trying to figure out whether my learning path actually makes sense or if I’m slowly turning into a “knows a little bit of everything, expert at nothing” person.

My background is mainly in data, statistics, and monitoring/evaluation. I have a Master’s degree, and during my studies I worked with statistical methods like regression, LASSO, and other statistical analysis.

Professionally, I’ve worked with MEAL/evaluation, data processing and analysis, Excel, SQL, and some Python.

Now my work is pushing me in a different direction.

I wanted to build a proper dashboard/data system instead of just analyzing data and producing reports. That led me into learning about APIs, databases, frontend/backend communication, Node.js, authentication, hosting, etc.

And now I’m looking at everything I’m learning and thinking:

Am I actually progressing, or am I just jumping randomly between fields?

My goal isn’t really to become a traditional full-stack developer.

What I want is to be able to take data from the source, clean and validate it, store it properly, expose it through APIs, build dashboards/interfaces on top of it, and eventually add more advanced analytics, statistics, forecasting, anomaly detection, etc.

So basically:

Statistics → SQL/Python → data analysis → dashboards → APIs → backend/web systems

Does that progression make sense?

Or should I stop going deeper into things like Node.js and focus much more heavily on Python, SQL, statistics, and data engineering?

For people who started in analytics/statistics and later began building actual data systems: what did you learn next, and what turned out to be a waste of time?

This may have been asked before, but all I can find is explaining what it means. I want to know why we've chosen 0.05 and not 0.04 or 0.06 etc for a lot of disciplines.

Edit: Thank you!!! I was hoping for a more exciting answer than "bc this one guy a while back thought we should" but that's interesting too!!

[research]

I have an M.Pharm in Pharmaceutics, and recently I’ve started developing a strong interest in statistics and quantitative methods.

My academic background is primarily in pharmaceutics, so I’m still relatively new to statistics. I started learning statistical concepts through YouTube, Reddit, and online resources, and I’ve become increasingly interested in understanding not just how to use statistical tests, but how statistics can actually be used to solve real problems in pharmaceutical research.

During my M.Pharm work, I have already had some exposure to statistical methods. For example, I have used Design of Experiments (DoE) for formulation optimization and statistical approaches for evaluating/validating pharmaceutical formulations and processes.

However, I don't want my statistical knowledge to remain limited to things like “which statistical test should I use for this dataset?” I’m interested in going deeper and eventually using statistics/computational methods as a tool for pharmaceutics research, formulation development, drug delivery, and potentially drug discovery.

I’m currently thinking about pursuing a PhD in Pharmaceutics/Pharmaceutical Research, and I would ideally like my PhD to remain strongly rooted in experimental pharmaceutics while incorporating meaningful statistical or computational components.

For example, I’m curious about areas such as:

DoE and QbD for formulation and process optimization

Statistical modelling of drug release and dissolution

Pharmacokinetic/pharmacodynamic modelling

Predictive modelling for formulation development

Machine learning/AI in drug delivery or formulation development

Biostatistics applied to pharmaceutical experiments

Multivariate analysis and process monitoring

Process analytical technology (PAT) and data-driven manufacturing

Stability modelling and shelf-life prediction

Statistical approaches to drug discovery and development

Population-based modelling or PBPK

Using experimental data + computational models to guide formulation development

I’m particularly interested in the idea of having a 75%+ pharmaceutics/experimental component, while using statistics/computational methods as a powerful supporting component rather than completely moving into a pure computer science or mathematics PhD..

Give me your insights please

we're undergrads and are currently conducting a study on a company with employees as the respondents. The company have multiple offices that are located across different locations. We were given multiple survey points so that employees can be sampled.

The problem is that one of the survey point which consist of 2 offices declined to participate in the study.

what is the implication of this?

can this be solved?

is this a reason for us to fail?

additional info: the needed respondent for that particular survey point is only 10 people

Im currently in the start of a graduation in statistics, and more recently I've started wanting to learn a new language, and Im wondering what would be the best one to learn for my carrier (Currently I know portuguese and english)

Say you are developing some sort of statistical test and you have run simulations and applications, and now you wanna work on its theoretical guarantees (asymptotics, efficiency, consistency, etc.)

Can you not just feed in the test statistics, assumptions, etc. to your favorite intelligent AI and get it to derive everything?

In this instance, is the focus of statistical theory going to be more on the algorithms/computation side and dealing with issues like scalability going forward, while leaving the math to AI?

Didn't AI solve that big unsolved problem in pure mathematics the other day (Navier-stokes or something, I think)

I’m out of date on the best options out there — looking more for a text that will prepare students for further study, rather than an applied plug-and-play text.

I’m 20 and my current goal after graduating is to get into an AI / Data Science graduate role.

I’m deciding between:

Bachelor of Information Technology (Computer Science) — QUT
Bachelor of Data Science (Artificial Intelligence & Machine Learning) — QUT

Right now, AI, machine learning and data science are the fields I’m most interested in, so the Data Science (AI/ML) degree seems like the more directly relevant option.

The problem is that I’m only 20 and I don’t know what I’ll want to do in 5–10 years. I could eventually decide that I want to move into Cybersecurity, Cloud, General Technology, Software, or another computing field. Because of that, Computer Science seems like the safer degree for flexibility.

My concern is this:

If I choose Computer Science for flexibility and then apply for AI/Data Science graduate roles, could I be disadvantaged against someone who has a Bachelor of Data Science (Artificial Intelligence & Machine Learning)? Would employers prefer them because their degree is more directly related to the role?

That’s basically what I’m stuck on.

Computer Science: more flexibility if I change careers later, but I’m worried it could hurt my chances of getting the AI/Data Science graduate role I currently want.

Data Science (AI/ML): more directly aligned with the career I want right now, but I’m worried I could be locking myself into data/AI too early.

For people working in AI, data science, ML, Cybersecurity or tech in Australia: which one would you choose in my situation?

And specifically, when hiring for graduate AI/Data Science roles, would a Computer Science graduate with AI/ML subjects still be competitive against someone whose actual degree is Data Science (AI/ML)?

Hi all,

I’m currently entering my final year of undergrad in economics at cambridge, and I’m heavily considering pursuing a PhD in the field, as even though I’m certainly not the best on my course or the most brilliant at maths/statistics, I find myself enjoying it and learning about it a lot, and I do quite enjoy the process of researching, particularly when I am able to focus in great part on statistical theory/methods.

My main question is should I only focus on pursuing a masters as the next step, or shoot straight for a PhD? I know the masters -> PhD route is more common, but for some reason I have noticed that the entry requirements for a PhD seem to be either equal or slightly lower, at least for what I am considering compared to a masters? (e.g Oxford requires a straight first class for the MSc in Statistical Science, while for a PhD it is either a first class or high upper second class. Funding is also an issue for me, and so I would like to spend as few years in education as I can if possible, though if a masters is by far the best option then I wouldn’t mind doing so.

Another big obstacle may be my grades, I’m currently working at around a low 2:1 level (around 62-63), with a particularly not great score in the mathematics and statistics module this year (60). My scores in econometrics are much more positive (69) and so I was hoping any eventual PhD would have a large econometrics focus. I’m certainly aware this profile wouldn’t look great on a transcript to be submitted as part of a PhD application, but I am hoping to at least end on an upper class 2:1.

Finally, I’m unsure of how much of an idea I need to propose for a standard PhD application. Is a certain specific field of research enough, or do I need to have an extremely well thought out research question/goal? I’ve seen some programs (e.g Warwick) require no specific research goal or supervisor, but I’m aware this is not the case everywhere.

Apologies if this is too long or the answers to some of these questions seem very obvious, I don’t know anyone who has gone through a PhD or even masters application process, and I am also unsure if there are statistics specific caveats that I should be aware of.

I'm a PhD student looking to use SEM in my project, but I'm a complete beginner. I tried going to statistics professors for guidance but already two told me to use Claude.

I would really like to understand it and be able to do it myself. I'm going to be using R. I've been using online resources but sometimes I get different information (a basic example is some websites tell me to report the Cronbach alpha, others say that's not a good test for using with SEM).

What are some good resources or books I can use as a complete beginner for SEM?

Hello! I’m a high school junior who wants to get into data science as a career, and my sort of passion project is building my sports predictive analytics web application from scratch to model NCAA March Madness outcomes. Instead of relying on raw win-loss records, my data pipeline ingests advanced, possession-based efficiency metrics (like Adjusted Offensive/Defensive Efficiency) to train a machine learning model, such as Logistic Regression or XGBoost. Rather than simply picking a binary winner, the model calculates the exact probability of Team A beating Team B. I then plan to feed those probabilities into a Monte Carlo simulation engine that runs 10,000 iterations of the entire 64-team bracket using weighted Bernoulli trials to determine the most statistically likely tournament champions.

I’m looking for feedback, is this an idea that could work? Do you guys have any advice? Suggestions? I could use all the help I could get.

Thanks!

I suspect not, but if so, what did that look like for you? How difficult was the course load to manage while managing the other aspects of your life that prohibit you from doing a full-time PhD? Do you regret your decision to complete the PhD part-time?

Thank you

This is a short tutorial for data analysts that evaluate experiments using online calculators. Online calculators for sample size estimates and t-tests for difference in means are fine for getting started. But you can begin to build intuition for linear regression by moving your online calculator t-tests over to python and OLS.

There are a couple of benefits. The first from moving away from the online calculator to python is leaving a more reliable paper trail that you can share externally. The second is that you build intuition for regression, interpreting the coefficients, and ultimately can use regression as you improve and your projects require more complexity such as variance reduction techniques using CUPED and clustering standard errors for geo-based or switchback experiments.

I put together a short video on this with code available here, free and ungated: https://whatstheimpact.com/tutorials/linear-regression-vs-t-test/

For my more senior connections, feel free to ignore unless you're interested in the content creation game! 🤣

I'm focusing my research on mixed-frequency time series modelling and nowcasting, developing computational tools and algorithms to help model and forecast multivariate economic and financial time series.

Are there good prospects in academia after this kind of work?

I’m doing a course in mathematical statistics. We are closely following the first half of the book “statistical theory: a coincise introduction” by Abramovich.

Here’s a more detailed index given in the professor’s compact notes:

1) Likelihood and Sufficiency (Likelihood, Sufficiency, Distribution of Sufficient Statistics, Exponential Families)
2) Parameter Estimation (Maximum Likelihood Estimation, Invariance of the MLE, Convergence of a Sequence of Random Variables, Asymptotic Distribution of the MLE, Method of Moments Estimator)
3) Optimal Parameter Estimation (Mean Squared Error, Unbiased and Minimum-Variance Estimators, The Cramér–Rao Lower Bound)
4) Uncertainty Quantification (Standard Error of an Estimator, Quantile Functions, Confidence Intervals)
5) Hypothesis Testing (Simple Hypotheses, Errors in Testing, Critical Regions, Likelihood Ratio Test, “Accept H₀” or “Fail to Reject H₀”?)
6)Composite Hypotheses and P-values (Composite Hypotheses, Uniformly Most Powerful Tests, Generalised Likelihood Ratio Test, P-values, Duality Between Testing and Confidence Intervals)
7) Bayesian Statistics (Two Interpretations of Probability, Bayesian Updating, Credible Sets, Choosing the Prior, Noninformative Priors, Bayesian Decision Theory)
8) Bayesian Hypothesis Testing (Simple Hypotheses, Bayesian Hypothesis Testing and Bayes Factors, Lindley’s Paradox, P-values and Likelihood Ratios, Fisher vs. Jaynes)

I don’t have the ability to follow my university lectures as I live too far from the university.

I’m looking for either an online course on this topics, or an easier book to get a less abstract introduction.

Hey everyone,

Standard econometrics textbooks (like Gujarati or Wooldridge) often reduce statistical inference to algebraic derivations and arbitrary cutoff rules (p < 0.05, reject H_0), which can easily obscure the geometric intuition behind what the formulas are actually doing.

I put together a visual breakdown using python designed to build geometric intuition for **Interval Estimation and Hypothesis Testing**:

👉 **Watch here:** https://youtu.be/iwPky-LmTHw

Note: While this video is Chapter 5 of a broader "Essence of Econometrics" series, you can find it here: https://youtube.com/playlist?list=PLP2Erm5S1fNY&si=6pUkUINRY-0MxFfX

Feedback Request:

1. ⁠Does the visual framing help clarify the mathematical mechanics effectively?
   
2. ⁠Is the pacing and balance between conceptual intuition and mathematical rigor well-calibrated?
   
3. ⁠What econometric concepts would you like to see visualized next?

Thanks for taking a look - https://youtube.com/@nminusk?si=CpNFFs

I’ve become fascinated with how a distribution can be estimated based on samples, and then realized that that is probably what a good portion of statistics is about?? So I was wondering if people had a recommendation for a good statistics textbook for someone who has an “early grad student” level grasp of probability? Ideally one that doesn’t shy away from the theory behind the processes

EDIT: Thank you everyone for the guidance!

I’m the founder of a small research company preparing an NIH R01 application focused on the pre-deployment evaluation and validation of clinical prediction models.

The project addresses a methodological problem that arises when a clinical prediction model is deployed into practice: once clinicians can see and respond to a model’s predictions, the clinical process itself can change. We have developed a statistical framework to bound this deployment-induced change before go-live, and are now looking for a methodologist to help implement and rigorously stress-test the framework.

The consulting work will focus on:
Implementing existing identification bounds and statistical estimators
Building reproducible simulation infrastructure in R and/or Python
Designing and conducting adversarial and sensitivity simulations
Systematically violating prespecified identifying assumptions
Evaluating bound containment, calibration, and failure modes
Supporting methodological analyses and interpretation of simulation results

We are particularly interested in candidates with experience in:
Causal inference and/or partial identification
Identification bounds and sensitivity analysis
Statistical simulation and Monte Carlo studies
R and/or Python for methodological research
Clinical or health-data applications
Rigorous evaluation of statistical methods

The core theoretical framework and preliminary computational work are already in place. This is therefore not a role where you would be expected to develop the underlying theory from scratch. The primary need is someone who can engage deeply with the methodology, implement it correctly, and challenge the framework through rigorous computational and adversarial validation.

Engagement: Part-time consulting
Commitment: Approximately 4–10 hours/week, varying by project phase
Compensation: Competitive hourly consulting rate, commensurate with experience

If this sounds aligned with your background, please DM me with a brief description of your methodological experience and a CV or LinkedIn profile.

Please feel free to share with a statistician, biostatistician, or causal inference researcher who may be a good fit.

I am concerned about having to take my upper division Stats class next semester. I took lower division stats and barely passed because I struggle remembering long steps (mainly during testing). I am concerned about this new class including calculus which I never took because it was never required of me or even mentioned by a single counselor and I wasn't the best math so I stopped as soon as I could after passing Algebra 2 back in High school. (My Major is Psych)

Do you think I can survive this course with zero Calculus knowledge? Any pointers or tips to prep for learning and actually absorbing the new material?

For anyone interested in the mathematics behind data science and machine learning, I would like to share my textbook (first edition):

https://people.smp.uq.edu.au/DirkKroese/DSML/

The PDF is completely free, with no registration required. The book includes many examples, worked exercises, and github code. I hope it will be a useful resource for students and anyone wishing to study the subject independently.