I want to rank several countries' characteristics (like economic power, military power, population...etc) in a logarithmic scale.

However, there are some countries that do not have any army (like Andorra) and in that category (and related ones) they have a value of 0. However the log(0) does not exist. So, how could I convert the values into something that could be used to make the logarithmic scale?

I have thought about two methods:

A) To every value in each category, I added the values of the "smallest" or "weakest" country (that does not have a 0 in any category, so that all categories have a non-zero number). In this case, I added the values from Seychelles to all countries.

B) To every value in each category, I added "1", so that in every category that the countries without armies have a "0", the minimal number is "1" instead.

I feel that the first method has the advantage of adding a proportionate quantity in each category, since you will see that for some categories all values are small (like 1,07; 2,08; 3,1...) and in other categories the numbers are much bigger (5, 20, 49, 80...). So in method A the added numbers are proportional to the values of each category

However, it feels arbitrary to add different numbers for each category instead of adding the same for all of them (i.e. 1). However, in this case, adding 1 to the small-values-categories seems to significantly modify these values, while in the case of categories with bigger numbers, the resulting number is almost the same as the original one (like, adding 1 to "1,1 "would increase it by almost the double: "2,1"... however for other categories with a number like "50", after adding the "baseline value", the difference is negligible: "51").

So which method do you think is more accurate?

These are the values: https://docs.google.com/spreadsheets/d/19zjwdoLum0yhIQ2v3m19kz5_IplwSJYZX6e0wMqMHoQ/edit?usp=sharing

Hi,

I am new to the field of Mixed Models (LMM, GLMM). I was hoping someone could point to some resources they recommend. Furthermore any resources about multiple comparisons would also be great!

Hey everyone,

In December 2024, I graduated from a low ranked SEC (Southeastern Conference) school with a BA in Mathematics, originally I had the intention to pursue a PhD in Mathematics, but some life circumstances came up and I have been working in Finance for the past 2 years. I really enjoyed Abstract Algebra and Probability and thought about doing something around there. However, over time and self study, I have gotten extremely interested in pursuing Statistics. The plan as of right now is to return to school by either Fall '28 or Fall '29.

It's an extremely interesting field where a lot of really interesting research can be done alongside nearly every field that has some form of quantitative aspect to it. Personally I have found myself really interested in Mathematical Finance and Econometrics mainly since I work in the area, but I also have some interest in biostatistics, especially related Epidemiology and Public Health.

The main downside is that my formal coursework is fairly limited, having only taken an Intro to Statistics course and a Probability course, but I graduated Magna Cum Laude and have coursework in Real Analysis, Abstract Algebra, Linear Algebra, and Differential Equations. The goal for me would be to apply to and complete a Funded MS and then use that as a launch pad into a good program, but I would not shy away from going straight into a PhD, as being a college professor has been a dream of mine.

I would love to connect with Statistics PhDs, Graduate Students, or people just interested in the field. What are some things that I should know about graduate studies? What are some good ways to prepare for graduate studies? What should I know about going into Statistics from a Math Backgrounds? Lastly, what are some good funded programs I should look into?

Thanks all!

I’m a social science PhD student doing HCI research and mostly use quant/experimental methods. I am considering a minor in statistics and want to understand whether this is something valuable in the industry job market as well.

UXR is usually the industry position most compatible with my field, but my understanding is that such roles rarely require advanced stats modeling and techniques except big tech, and the market is shrinking.

If in the worst case, that I couldn’t find industry positions directly relevant to my main field, would the stats minor help in getting data-related jobs (say, compared to a stats BS or masters)?

For context, I am considering applied stats minor, and it requires 5 grad-level courses in stats (regression at matrix level, probability, inference, and 2 other specializations).

I have been working on causal inference and experimentation frameworks within the e-commerce tech industry lately, specifically dealing with platform behavioral and transaction telemetry data to determine how customers behaves on those e-commerce platforms.

Lately i have been facing problems where standard textbook statistical theory fails when dealing with messy, real-world data distributions as human behaviour is unpredictable and many unexpected things, which i didnt even thought could happen, can happen in the real world. Such as product delivery delay, causing the customers to be angry and hence put them off from buying the product, each of these variables have to be accounted for.

To model and have these unexpected psychological state among customers accounted for, we use variables such as sudden spikes in customer support chat volume, rapid page refreshes, or immediate session drops.

Also, Marketing headers and automated CRM systems love to push blanket promotional campaigns, but as statisticians, we know that measuring simple unadjusted A/B lift is a massive selection-bias trap.

I been trying to come up with a Statistical model that is trying to model complex human behavior—specifically isolating true customer revenue incrementality from dead-weight margin cannibalization. Essentially, trying to answer: Did the coupon cause the revenue spark, or did we just hand a discount to a power-buyer who was already determined to buy anyway even without the vouchers?

Are people in the tech industry genuinely valuing such skills nowadays? Using statistics to model and predict human behavior and intent, answering questions like does our vouchers and ads really work?

I would love to hear your experiences how do u guys deals with the unpredictable messiness of human intent.

From what I understand, correlation only looks for a connection between two variables, while regression explicitly looks to see if one variable's increase/decrease positively or negatively impacts another variable's increase/decrease.

Hi! This is my second semester studying Statistics at a top university. However, I came from a poor high school education, and I wasn't used to studying regularly or keeping up with the university workload. As a result, I failed Calculus I.

I realized that I urgently need to change my study habits. I also want advice on how to do well in my degree and, most importantly, truly learn the subjects, especially considering the use of AI and other tools.

I would love to hear your thoughts on how I can become a better student. What study methods, habits, and strategies do you recommend?

Thank you!

Hey, I have a decent background for distribution theory and statistical inference. I wanna get into random processes. I have no idea about the resources to start with. Can anyone suggest me some ?
Moreover, are there any discussion groups and channels on telegram ir any other platform specifically for this?

I’m considering taking a statistics course, and I’ve been thinking about the next steps—whether it’s significantly more beneficial for my career to pursue a technical specialization, a master’s, or even a PhD

I did a personal side project on why some years bring much worse smoke haze to Singapore. I'd like people who know stats to tell me where the design is weak before I show it to anyone in the field.

The chain I test, one observation per year:

1. Pacific El Niño index (June to August) vs Kalimantan rain (August to October), 1979 to 2025, n = 47. Pearson r = −0.79.
2. Sumatra rain regressed on the El Niño index and the Indian Ocean Dipole index, n = 47. I test the Dipole coefficient.
3. log10(fire detections) = a + b·rain + c·post2016 + e, one model per island, 2001 to 2025, n = 25. post2016 is a dummy for a fire-prevention policy shift after 2015. R² is 0.82 and 0.89.
4. Fire detections vs Singapore's seasonal mean PM2.5, 2016 to 2025, n = 10. Spearman rho = 0.88, bootstrap 95% CI 0.47 to 1.00.

I applied a Holm correction across these five tests, used 10,000 paired bootstrap resamples, ran leave-one-year-out, and swapped data sources as robustness checks. All five tests stay significant.

What I'm unsure about:

1. I wrote the analysis plan after I had already explored the same data. I say this openly, but is there anything more I should do besides waiting for future seasons as a true holdout?
2. The Sumatra fire model has a Durbin-Watson of 1.44. Should I switch to Newey-West errors or an AR(1) model, or is the bootstrap enough here?
3. Is OLS on log10 counts acceptable, or should this be a negative binomial GLM?
4. A dummy for "after 2015" vs a linear trend: both give the same sign. Is there a better way to handle a policy break with only 25 years?
5. With n = 10, is rho = 0.88 worth reporting at all, or should I present it as descriptive only?
6. The five tests form one causal chain, so they aren't independent. Is Holm the right correction, or is the correction itself the wrong framing?

Code, data and a 12-page write-up: https://github.com/Shelikeit69/haze-drivers-research

I built this with an AI coding assistant, which is part of why I want people to check the stats. Blunt answers welcome.

Helloo:) I’m working on fatty acid composition analysis (from bird preen oil samples), with very little previous knowledge of statistics, and I’m looking for some help understanding what’s happening.
Background: I have a dataset with treatment group, sex, aviary of the sampled bird, and 10 fatty acids found in the preen oil samples. There are 3 treatment groups: germ-free, conventionalised, and parent-raised (although only the germ-free and conventionalised can be realistically compared). Between these 2 treatment groups, there are only a total of 27 samples, 13 in one and 14 in the other. The fatty acid cells contain proportions rather than absolute values (they always sum up to 1), so an example row looks like this: 

The main end goal is to see if the germ-free condition results in any changes to preen oil.

Now, for the analysis issues: I’ve computed robust Aitchison distances, which are supposedly a better choice for this data than Bray-Curtis(?) and ran a PCoA with them, followed by PERMANOVAs for treatment group, sex, and aviary (none of which were significant, unlike the marginally significant and high R2 results on B-C). 
I’ve also been advised to run some form of a linear model on each individual fatty acid, but I don’t quite understand where to start with that… Or which one would be suitable.
I would appreciate any feedback, comments, or advice on what I’ve done so far and what I’m trying to do!:)

I've spent the last weeks building a strict research pipeline for BTC/ETH spot trading (EUR pairs). Everything has been tested on historical data and in paper trading, with no real money yet. My methodology is probably stricter than most hobby setups. The result: nothing survives. I'd like to hear from people who got further, or who hit the same wall.

My setup (the rules I hold myself to)

• Criteria are written down and committed to git before each test and never changed afterwards.
• Costs are always included: 0.25%/side in early tests, now 0.10%/side (taker), plus slippage.
• Strict walk-forward. The result has to hold per year and per market regime.
• Placebo test: 1,000 block-bootstrapped fake price series. A strategy only counts as "proven" at p < 0.01.
• Bar: beat buy-and-hold after costs and have a smaller max drawdown, with 99% intervals and a Bonferroni-style correction for the number of attempts.
• A held-out "vault" dataset (Bitstamp 2018–Mar 2019) that nobody has looked at. Each strategy gets exactly one shot at it.
• A hard cap of 13 attempts in total. If nothing works by then, my conclusion is "not feasible at these costs".
• No LLMs in the trading decision. They have seen the future in their training data.

What I tested (all failed)

Other findings:

• 0 of 240 flexible-exit variants were positive, and 0 of 270 "conditions" from a trade journal were positive. The exit isn't the problem. The entry has no edge.
• Paper trading confirmed this. A dip rule went 10/10 winners in one night, but that was one single recovery move, not 10 independent trades. The backtest over 7 years says it's placebo-level.
• After FOMC decisions crypto volatility roughly doubles in the following hour (BTC 0.66% → 1.25%), but the direction is unknown, so there's nothing tradeable.
• Lead-lag between exchanges: the lead I measured is ≤ 0.06%, far below the cost of a round trip.
• With ~0.2–0.5% friction per round trip, even a near-perfect predictor at horizons under 1 hour earns almost nothing.

Where I'm stuck: my real questions

1. Source of edge. For a retail trader without colocation or speed, what edge in crypto spot is documented to survive 0.1–0.2% costs? Is it structural (liquidation cascades, funding imbalances, forced selling, order book imbalance) rather than price patterns?
2. Is my bar too strict, or the right one? p < 0.01 against block-bootstrap placebos, plus beating buy-and-hold on Sharpe and drawdown. How do serious shops set this bar for single-asset strategies?
3. Beating buy-and-hold in crypto. BTC/ETH buy-and-hold has a huge drift. Should a timing strategy be benchmarked against risk-adjusted buy-and-hold, or against cash?
4. Regime dependence. Many strategies seem to work in one regime only. Is it legitimate to run a regime-switching approach, or is that overfitting in disguise?
5. Paper vs live. How far did your paper results drift from live (fills, slippage, latency)?
6. Has anyone gotten past this point? Not "my backtest is great", but sustained live profit after costs on BTC/ETH spot. What was the edge, and how long did it last?

What's running now: paper trading 24/7 on real exchange data, with 42 strategies in a "sandbox" that each have a pre-set required number of trades and a deadline. I'm also collecting order book, liquidation and lead-lag data for months before forming new hypotheses.

I'm happy to share the methodology. Honest stories about what didn't work for you are especially welcome. That's the rarest information online.

I am performing an ordinal regression analysis reduced to 10 predictors 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 oriwith 10 predictors, reducedginal 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 and sequentially add a predictor, then 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 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

greetings everyone, I am a grad student from forestry background and i am doing a research project on biomass estimation via sentinel 2 variables. I have 47 plots with field measured biomass value and i ve analyzed 18 different remote sensing variables that acts as the predictors of biomass. predictors correlated well with the measured biomass but the regression model stated lower R square value (i.e. 0.40) at which isnt like i expected. I ran five different models MLR, PLSR, RF, GBR, linear SVR, Ridge (im a beginner in coding and all, so claude helped me) etc but neither improved the coeft of determination. I followed the exact methodological framework from the literature but lost somewhere. My main objective is to publish the paper but the lower accuracy is becoming the problem. Sorry if i couldnt explain it clearly, feel free to ask via comment or dm/ any suggestions and feedbacks are appreciated. Thankyou!

Hello!

I have minor revisions on my scientific paper. One of reviewers comment was: "please include the statistical model equation in the Methods section.". My doubt is: I performed a Beta Regression Model in R: model_beta <- betareg(proportion ~ plant + week, data = plants).

Is this what I´m supposed to add to the paper? Im a biologist, not a statistician, so im not sure what to write ... Thanks!

Hi everyone, just want to preface by saying i am very much a microbiologist and know extremely little in the world of stats. I’ll give some context to my experiment: I preformed an enrichment where i grew bacteria in vials (n= 3) for 6 weeks, then took a portion of those growths and added it into new vials (n=3) for another 6 weeks. The vials let’s say A1, B1, C1 corresponded to the second batch of vials A2, B2, C2 where in the samples from A1 was added to A2 etc. I did this at two different temperatures (20 and 30 degrees). So i have two variables, the time and the temperature, giving me four groups.
Group 1. 20 degrees celsius week 6 (rep1, rep2, rep3)
Group 2. 30 degrees celsius week 6 (rep1, rep2, rep3)
Group 3. 20 degrees celsius week 12 (rep1, rep2, rep3)
Group 4. 30 degrees celsius week 12 (rep1, rep2, rep3)

I have species level relative abundance data from shotgun metagenomic sequencing and want to determine whether community composition differs between the four groups and ideally a post hoc specific to which. The complication is that at each week 12 culture was subcultured from its corresponding week 6 culture with temperatures maintained. Therefore, week 6 and week 12 are linked observations rather than independent samples. There are three lineages per temperature. I’ve tried Bray curtis PERMANOVA in R using vegan::adonis2() with restricted permutations but the permutation schemes i’ve tried don’t test the temperature, week and interaction effects as intended. I also found significantly different multivariate dispersion between the temperature groups.

So my question is:
1. What statistical method would you recommend for testing community composition while accounting for the linked time points?
2. Is there an appropriate permutation scheme or alternative model for testing temperature, time, and their interaction with this design?
3. What methods would be suitable for identifying differentially abundant species from these metagenomic data?

I’m looking for a statistically defensible approach used in similar microbial enrichment or longitudinal studies. References to relevant papers or example R code would be greatly appreciated. Thank you!!!

Context: I have to send my final dissertation manuscript to my committee tomorrow. My chair called me this morning and told me my stats are all wrong because I ran too many tests with too small of a sample. When I asked for guidance on resolving this, they said "You want a PhD? Figure it out". I have spent all morning reading resources and I have gotten myself more confused.

This is a single group pre-post design based on feasibility and exploring clinical outcomes. I originally had planned paired samples t-tests, but I ended up with such a small sample that it would inflate error rate. Also because of the small sample, any assumptions of underlying distributions can't be met, so I'm using exact sign tests. I modeled my stats after another published pilot study with 7 participants and 15 comparisons. They do not do a correction.

I ran the 7 exact sign tests for my outcome variables, found significance in 6/7, and also presented the data visually. If I did a Bonferroni correction to control for multiple comparisons, my new p-value would be .007 and basically impossible to get with exact sign tests. Is my only option to only present stats for my main variable, and only present descriptive/visuals for the rest?

Suppose two forecasting models have nearly identical average prediction errors, but those errors evolve very differently over time.

In one model, errors are approximately random. In the other, they form persistent clusters, oscillations, or systematic transitions.

Could the temporal structure of prediction errors provide an early signal of changes in the underlying system, even before conventional change-point detection identifies a regime shift?

I'm particularly interested in whether anyone has investigated this using autocorrelation, entropy, or multiscale analysis of prediction errors.

What statistical test would you consider most convincing for evaluating this hypothesis?

I had this idea based on something I've noticed: people often assume that others think the same way they do, and that their own opinions reflect what the majority believes.

So I thought, why not turn that into a game?

The idea is to test how well you can predict what the majority actually thinks, and see whether your own opinions are as popular as you might expect.

One thing I’m still unsure about is how many players would be needed to get a statistically meaningful representation of the population for each question. I don’t really know much about statistics, but it would be interesting to figure this out so I can estimate how many players are needed for the game to work properly. 🤔

Was he into older sisters, like onee-san? ._.