I'm a psychology student and I'm trying to figure out if it is appropriate to run a t-test on my data. I made some QQ plots to check for normality, and I'm not 100% confident in interpreting them. It seems the data don't deviate massively from the line, but at the same time, it's not the case that all points fall very close to the line, so I'm a bit conflicted.

I'd really appreciate it if people could let me know what they think! Each condition has n=50, happy to provide more details if needed.

https://preview.redd.it/qgdv125pdcwg1.png?width=1491&format=png&auto=webp&s=b2baea8730d3070a04d8bcd2920cba3af5c0073d

Same graph as above but y axis changed to reflect the maximum and minimum score possible

I’ve been researching the problem of test-retest reliability for a while now and I’m curious how others are handling the identifiability issues that come with longitudinal data.

In psychology we are usually taught that retest reliability is a simple correlation between two time points. The problem is that this assumes the underlying trait is perfectly stable and the measurement error is completely random. In my opinion these assumptions are basically impossible for real world data because even the most stable traits usually only correlate at about 0.6 to 0.8 over time.

I recently published a paper in Applied Psychological Measurement where I demonstrated that when these assumptions are not exactly met the resulting retest coefficient is entirely uninterpretable. Moreover, these assumpions are also not testable, since the framework is essentially a black box. A simple correlation cannot tell you if a low score means your scale is noisy or if your participants actually changed, because you only ever observe two knowns, but have more than two unknowns.

I am definitely not alone in this critique. A paper that came out earlier this year by Tufiş, Alwin, and Ramírez in the Journal of Survey Statistics and Methodology reaches a similar conclusion using GSS survey data. They argue it is a bit of a Catch-22 where we rely on these coefficients because they are easy to calculate even though the math is often fundamentally uninterpretable for most psychological and sociological constructs.

The classic fix for this is the Heise 1969 framework. If you have three waves of data Heise showed you can algebraically separate reliability from stability using the three observed correlations. It is a neat trick but as I’ve dug into it the limitations are pretty glaring. It requires constant measurement precision across waves and a strict Markovian process for trait change. More importantly with only three waves these assumptions are mathematically untestable so you are basically just trading one set of blind assumptions for another.

I am looking to move past the 1960s-era CTT math on this. I am wondering if anyone here has found success using more modern latent trait models or SEM-based approaches to reliably differentiate trait stability from measurement error. Specifically, I want to know how people are actually implementing Latent State-Trait models when they don't have massive multi-indicator datasets. Are there Bayesian or Dynamic SEM approaches that allow us to identify these components without needing a ridiculous number of waves? I would love to hear if there is a better modern standard I should be looking at that moves beyond the Heise framework.

My paper: https://journals.sagepub.com/doi/full/10.1177/01466216251401213

The Tufiş et al. 2024 paper: https://academic.oup.com/jssam/article/12/4/1011/7484622

Hi, so a rundown of what my experiment is: I conducted separate 96 hour duration toxicity tests with three different life stages of a benthic amphipod: neonate , juvenile, and adult. The tests were exposing them to various mercury concentrations to assess mortality. So my two way anova was conducted with two factors: life stage and mercury concentration on mortality. I kept reading that arcsin transform on percent mortality data is used with these types of tests to improve normality and variance so when I arcsin transformed my mortality data I ended up getting that my normality (Shapiro-wilk) failed (p < 0.05) and equal variance (brown-forsythe) also failed (p < 0.05)

Now this is where my question arises …

I decided to run the anova with the raw , untransformed mortality data and it still failed normality but passed equal variance (p = 0.076)

Do I just go with the raw data and then say that even though normality was violated, results were retained due to robustness of ANOVA?

Also, one last thing I did try and log transform (ln) my mortality data and run the ANOVA but it didn’t really work with my data

Any feedback would be appreciated plz and thank you 😭

So we were solving the following problem in my Inferencial Statistics class the other day:

We wish to estimate the variance of the nistamine concentration in an ointment. It is known from long time experience that it's distribution follows a normal distribution. A sample of 9 ointments is taken, yielding the following nistamine levels (in millions of units/g): 1, 0.9, 1.5, 2.8, 3.1, 3.2, 2.5, 1.9, 2. Estimate the variance using two confidence intervals at the 99% and 95% confidence levels.

Thing is the teacher gave us the standard formula for Confidence Intervals of Vartiance- (two tailed)

https://preview.redd.it/sndsnz8nbawg1.jpg?width=750&format=pjpg&auto=webp&s=48b9cce1bf8be5ecdbd7a8683f6dc391b9550f74

I solved the problem using the formula as it is, multiplying the (n-1) by the variance (because the problem is talking about the variance s^2), however, the teacher and the rest of the class got a different result. When I asked the teacher why we got different confidence intervals she said it was because in this specific case we were talking of a sample, therefore, we had to multiply by standard error of the mean and not variance.

I thought this was super weird because I don't think i'd ever seen a formula of confidence intervals for the variance where they did this; of course AI is not the best source with these things but I asked Chat GPT about this and it agreed that it was NOT common to do that, in fact, rather weird that she did it that way.

I want to get a more experienced or detailed explanation on this to see if I'm just ignorant on the topic or if she did just do something weird.

Why do we need to know the deviations from the mean to compute the variance?

What is the logic behind this? Why not use any other data point like the median or mode or anything else? What if the mean is already skewed as it’s pulled by an extreme outlier?

When it comes to the general spread, even beyond the variance, why use the mean when the mean itself is unreliable with outliers?

How are these two related?

Hello, the title basically says it all, but I’ll be going to a solid program for statistics, but still not top-tier as far as “reputation” for statistics departments go (the department is I think top 35 for statistics on US News). I am somewhat worried about my job opportunities afterwards.

I have heard mixed things about this. For clarity, I am unsure as to what I want to do after my PhD as of this instant, so I’m thinking about both (1) academic jobs and (2) non-academic jobs.

(1) For academic jobs, some people say it matters tremendously where you go to school and others saying it matters more about your advisor and your work that you do during the PhD. I’d like to think the latter is more true (for example, would a university really value you more if you went to say, a mega-elite stats school but your work and time you spent there is not impressive than if you got strong recognition for your research but went to some not as well known institution? I would like to say no). I’d like to also think that part of the reason we see so many professors coming from elite schools is because the elite schools take in better talent than any other schools, and the reason these people from the elite schools actually get an academic job is because their research is impressive and they had a good advisor, not exactly because they went to some fancy school. Of course, it may be easier to do impressive research and have a well known and solid advisor from a fancy school than say a mid-tier. This is just pure speculation from me, and I’d like to know what other people think.

(2) I’ve heard that for non-academic jobs, these sort of rankings don’t matter as much (you can correct me if I’m wrong here). My stats department is also known for there connections in industry or out-of-academia institutions, so this is not as much of a concern to me.

So, from my knowledge, I’m mainly worried about struggling to find a job if I decide to go the academic route than any other route.

I appreciate any input on this, thank you!

I'm building a composite health index that combines periodic blood biomarker data (every 4-12 weeks) with continuous wearable sensor data (daily) into domain-level health scores. After an external methodology review, I've resolved some initial issues but have new questions. Context:

What I've settled:

• Evidence weights from per-SD mortality hazard ratios (all HRs converted to per-SD scale before computing ln(HR))
• Reliability weights from CCC/ICC (not MAPE — switched after review showed MAPE conflates systematic bias with random noise)
• Geometric mean combination: √(We × Wr) — confirmed as defensible by reviewer
• Four independent health domains (no composite average across domains)

Where I need help:

1. Blood-wearable signal non-independence. In my metabolic domain, blood HbA1c and wearable step counts both encode insulin sensitivity signal. Google's WEAR-ME study (Nature 2026) showed wearable features explain 43% of HOMA-IR variance. I blend blood and wearable into one domain score with time-decaying weights (blood dominant when fresh, wearable dominant when blood is stale). Should I apply a correlation discount when the two signals share latent variance? If r(blood_score, wearable_score) > 0.45, what's the principled adjustment — reduce effective contribution by r/2? Or is there a better approach from multivariate composite construction?
2. Regression to the mean in a pre-post health monitoring system. Users who start monitoring because they feel unwell will have systematically worse baselines. Even without intervention, their scores will improve on retest. I'm planning ANCOVA correction (Corrected_gain = Observed_gain - (1-r_test-retest) × (Baseline - Pop_mean)) for backend analytics. Is ANCOVA sufficient, or should I also use Lord's paradox–aware methods? And in the user-facing display: should I suppress trend interpretation for the first 2 test cycles, or show it with a caveat?
3. Single-marker domain precision. One of my domains has only one blood marker (an inflammatory biomarker with intra-individual CV ≈ 44%, ICC ≈ 0.62). After log-transformation, effective ICC improves to ~0.70-0.75. I display a confidence band on this domain's score. Is there a minimum reliability threshold below which a single-marker domain score should not be shown at all? Or is the confidence band approach sufficient for a wellness (non-diagnostic) product?
4. Collinearity within a domain. Two of three blood markers in my metabolic domain share variance by design (one is mathematically derived from the other). VIF analysis is planned. If VIF > 2.5, should I discount the derived marker's weight, or is the intentional emphasis on the shared signal (glycemic control) defensible if clinically motivated?
5. Score normalization reference. I'm using a large US population survey (N=7,840) for age/sex-stratified z-scores. My target users are health-conscious Europeans aged 30-55 (BMI <27, no diabetes). What's the minimum overlap between reference and target population before normalization becomes misleading? Is sub-sampling the reference to match the target profile the right approach, or does that introduce selection bias?

Like if I publish 2/3 papers of good websites like famous websites will I get a 100% scholarship for my masters/PhD and to get one what topic should I do my research on?

The returning soldier effect to me seems more or less representative of the slightly higher chance of a man being born exaggerated by a post war baby boom.

You can even see it followed through to relatively peaceful times. Where, there is a way higher population giving birth. Way more babies popping out. Maybe we didn't need biologists but statisticians for this?

We recently had a first course in statistics, where we covered the usual soup of confidence intervals, MLE, hypothesis testing etc. etc. but the most interesting thing were bootstrapping and Jackknife which just seem to "work". Upon asking the instructor we were told that to fully understand why these work we'd need to devote a whole semester to just these. Coming from a pure math background, stats never sat with me but this has to be one of the most beautiful things I've ever seen in this subject! I really want to try to have a go at it so can you please give me a roadmap to a "proof of why bootstrapping work"? You can assume the standard undergraduate curricula concerning probability theory, analysis, linear algebra etc.

As in the calculus derivations behind everything in statistics? Does anyone love exploring the math engine behind the formulas?

Does anyone try to break the formulas and add their own flavor to see what happens?

Does anyone question what happens if we stop taking the square root of a variance, for example, and start using absolute values for fun?

Is this a valid hobby or a sign you are an outcast?

I review a fair amount of applied quantitative work and I keep running into the same pattern: tables full of p-values and significance stars, but no standardized effect sizes, no confidence intervals around the estimates, nothing that tells you whether the effect actually matters in practice. A regression coefficient of 0.002 with p < 0.001 tells me the sample is large, not that the effect is interesting.

I know the ASA put out a statement on this years ago, and I've seen plenty of arguments for reporting effect sizes. But the practice hasn't really changed in a lot of fields. Is there a reason people still find p-values alone informative? Or is it just institutional inertia at this point - reviewers expect stars, so authors provide stars?

I am a PhD student conducting a meta-analysis. I have already extracted the data from each paper and calculated standardized mean differences (SMDs) for all my outcomes. The next step is the actual meta-analysis, which will be a robust, random effects meta-analysis (robumeta in Stata) to account for differences in study settings and multiple outcomes per paper. Here lies my question. I am unsure which effect sizes to include in the analysis. Most papers measure the outcome variable as an overall index as well as separated into sub-indices. Some papers use a different estimation method as robustness checks or carry out sub-group/heterogeneity analysis. Others create the index in two different ways and calculate effects for both. Lastly, some papers estimate the effect on the main outcome for multiple follow-up periods separately as well as pooled over all periods.

Would I include all potential estimates of a paper in the meta-analysis? For example, the overall index and the sub-indices, the outcomes for the overall sample as well as for sub-samples and so on. (My analysis includes 28 papers if that is relevant.)

Thank you very much for your help! If you can refer me to further reading about this, I would also be very happy!

I have to take stats for the nursing program through my local college, the workload from professors is insane. But I need 5 credits. UOP is 3, so I need 1&2. Has anyone ever taken these classes through them?

https://preview.redd.it/iydfgzr034vg1.jpg?width=2736&format=pjpg&auto=webp&s=2a3d227264cfa1cf4712da311a5a4911c5fbface

Heads up, folks!! we’re doing something special - an AMA with Lior Gazit & Meysam Ghaffari, authors of Mastering NLP from Foundations to Agents, happening on Friday, April 24, 4:30-6:30 PM ET over here on r/LLMeng.

Lior and Meysam don’t just talk about NLP, they connect the dots from core language fundamentals to modern agent systems. From designing scalable NLP pipelines to building RAG workflows and agent-based architectures, they’ve been working on the exact challenges many of us are facing right now.

🔍 What makes this AMA worth your time?

• They go beyond surface-level GenAI and dive into how NLP foundations power LLMs, RAG, and agents
• They bring real-world experience building and deploying ML/NLP systems where performance actually matters
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📚 Get a Head Start

If you want to get the most out of this AMA, take a look at their latest work: Mastering NLP from Foundations to Agents
🔗 Buy Now - https://packt.link/fCmpl

This book walks through the full journey, from embeddings and transformers to RAG systems and agent workflows.

📌 AMA Details:

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🗓️ When: AMA goes live Friday, April 24, 4:30-6:30 PM ET
📝 Submit your questions here before April 22

Let’s make this an AMA worth remembering.
Drop your best questions. We’re excited to see what you come up with.