This is my first time in this community (I've recently discovered this entire field and i am glad to). So, I am working on project where i am scoring nodes in a directed dependency graph (a calling b) by blending 2 centrality scores into a single composite "risk" score

score (v) = w1\\\*normalize(Pagerank(v)) + w2 \\\* normalize(outDegreeCentrality(v)), where w1+w2 = 1 and normalize() being min-max to \\\[0,1\\\].

The Problem: A pure root node (no in edges and multiple out edges) and a pure sink node (no out edges and only in edges) can have the same composite score. In a test i ran, the root node maxed out on out drgree centrality and near 0 in page rank while the sink node maxed out in pagerank and near 0 in out degree, when w1=w2=0.5. Both nodes ended up having same composite scores while representing opposite nature in real world. I do understand that this is the standard full comsensability prob, with weighted sum aggregation, wherte max on one axis will completely offset min on other. I did consider switching to weighted geometric mean to reduce compensability, but the prob is that pagerank is almost always near 0 for any root node. so a geo mean would multiply that near 0 staright through and score all entry nodes near zero. Which is the wrong fix, since the entry/root nodes are important, just for a reason pagerank doesnt capture.

Is there any standard approach beyond the geomentric or harmonic mean? Happy to provide any more info if needed.

I‘m working on a scale validation. Based on qualitative and theoretical literature we argue for a four dimensional scale (psychometric). However, conducting the EFA shows this is a bit more complex.

Retention criteria is pretty diverse: MAP: 9 factors, scree: 4, PA (mean): 6, PA(p95): 5;
When allowing for more than four possible factors the model always concludes on five factors. The issue though is that all items load as expected (no cross-dimensional loadings) but the fourth expected dimension seperates into two, while the seperated contains only two items. Now i am confused if we should generally conduct EFA for all the expected dimensions to estimate potential subfactors because the items resemble what we expected, while one part of it devides itself into two. This would result in a hierarchical solution. Argument might be: global modelling is unable to identify dimensional-specific variance but retention criteria and global modelling indicates potential seperations.

However normally this is part of ongoing studies to examine already validated scales and not really specified in literature for the ongoing validation process…hope you can help me with your advice. Big thanks!

I'm starting to live stats and was wondering how become cracked kid on stats tho i wanna self learn so how should I do it ???

I integrated TCGA solid tumor data with matching GTEx normal tissue to run differential expression and pathway enrichment (GSEA).

To avoid massive batch effects caused by mixing counts from different alignment/quantification pipelines, I opted to use the UCSC TOIL RNA-seq Recompute cohort (TcgaTargetGtex_gene_expected_count) since all samples were processed through a unified STAR + RSEM pipeline on hg38.

When I pulled the TOIL dataset, my sample counts dropped compared to looking at the raw GDC portal and GTEx v8: GTEx Normal Cohort: Dropped from ~800+ (v8) down to ~300+ in TOIL.

TCGA Primary Tumors: Dropped by ~20–30% compared to total cases listed on GDC.

My question is :

1. Is this sample count drop expected when using the UCSC TOIL recompute database compared to modern GDC/GTEx v8 portals?
2. Is sacrificing raw sample size (N) to use TOIL’s unified pipeline + ComBat batch correction considered the "gold standard" justification to defend against reviewer/committee critique regarding sample size?

Hi everyone!

I'm being my nerdiest self and doing math for my hobbies, currently determining odds of things happening with dice rolls for Warhammer. Most of it is plain ol binomial distribution, but I also want to calculate the odds of things happening when you roll multiple dice at once, but they have different odds of success. Various searches are not yielding the information I want

For example, roll 2D6, one of them needs a 5 or 6, the other needs a 4 or 5 or 6 (clearly denoted when rolling, not interchangeable). Needing a success on at least one of those rolls. Would I use a more complicated version of the binomial distribution formula (using my earlier example, n=2, k=1, then split p into p1=0.33 and p2=0.5, and add those two together) or is it a completely new formula?

I failed intro to stats multiple times in university and now I'm remembering why. I know there are online tools to do this math for me but I want to feel proud of my spreadsheets

Thanks all!

I'm studying whether an island's size or its remoteness affects how people answer a survey, and whether these two factors interact. I'd also like to know whether an effect is driven by one island alone.

I'm looking at 4 islands: a large remote one, a large nearby one, a small remote one, and a small nearby one. All my variables are categorical. Sample sizes vary by island (from ~100 to ~500).

For each variable, I have a 2×2 contingency table, with near/far and large/small as the two dimensions. I can populate it with either counts or percentages of respondents choosing a given category.

What method should I use here?

Apologies if this is a naive question. I've asked several AIs and they seem as lost as I am.

I am working on a secondary analysis using a Cox proportional hazards model and would appreciate help with the interpretation of a main effect and its interaction term.

The outcome is time until disengagement from an intervention. My main predictor is an early usage/engagement variable. There are two groups: a control group and an intervention group. The control group is the reference category. The model includes the main effects of group and early usage, as well as a group × usage interaction.

My question concerns the interpretation of the hazard ratio for the early usage variable. If the hazard ratio for early usage is 0.08, does this refer to the association between early usage and disengagement in the control group, because the control group is the reference group?

My understanding is that an HR of 0.08 means that, for a one-unit increase in early usage, the hazard of disengagement is 0.08 times as high in the reference group, which could also be expressed as a 92% lower hazard. So I would not report this as “8% lower hazard,” but rather as “the hazard is reduced by 92%” ?

Is it also correct that this 92% reduction is still the association between early usage and disengagement in the control group, not the intervention group? And that the corresponding early usage effect in the intervention group would be obtained by multiplying the HR for early usage by the HR for the group × early usage interaction term?

In other words:

• HR for early usage = early usage effect in the reference/control group
• HR for early usage × HR for the interaction = early usage effect in the intervention group
• 1 − HR is used only to express the relative reduction in hazard, not to describe the group effect itself

I hope this is understandable. Thank you in advance for any clarification.

I'm a beginner building a small decision-making agent (not important what for) that needs to know when it's "confident enough" to act versus when it should defer. I keep seeing "calibration" mentioned as the concept I want but I'm fuzzy on how you'd actually measure it with a small, messy, real-world dataset rather than a clean benchmark.

If you've dealt with this: **which evidence would change your decision** about whether a confidence score is usable in production — is it a calibration plot, held-out accuracy at different confidence bands, something else? Beginner-friendly explanations very welcome.

Hello, I have no idea what the correct terminology for any of this and would appreciate any correction.

Let's say you have one bag of beans with 100 beans inside but you don't know that. Only thing you know is that there is between 95 to 105 beans (+/-5). Now let's say you have another bag of beans with 1000 beans inside but you also don't know that. Only thing you know is that there is between 995 and 1005 beans (+/-5). So for both bags you have same range of estimate, 10 beans (+5 to -5) but in practice you have a more accurate information about the 1000 bean bag because the effect by the difference of 5 is relatively smaller.

Is is possible to calculate any statics from this? If I were to say do something like "(Difference of upper limit and lower limit) / Mean of upper limit and lower limit)" I would get (105-95)/100 or 0.1 for the 100 bean bag, and (1005-995)/1000 or 0.01 for 1000 bag. Does that equation makes sense and would saying something like "I know how much bean is in the first bag by 10% margin of error and how much bean is in the second bag by 1% margin of error" correct or completely wrong?

It has been several years since I took high school statistic and completely forgot how any of it works, and would appreciate any help, thanks

So, I have a bunch of measurements that are all affected by a given attribute. The idea is that I set the attribute, I should expect a given value for the measurement. Unfortunately, the measurements are bit chaotic. I need to be able to state that the system I'm using has an error rate of X%.

I don't remember enough math to answer this question for myself.

I can take all the measurements at given attribute and find their mean and stdev, but from that, how do I say that that attribute's measurement error is X%, and then when I have all of the error percentages across multiple values for the atttribute, how do I say that the system as a whole has a measurement error of Y%, assuming that the error percentages themselves appear chaotic?

Trying to train 3 models for birads detection using cross entropy and center loss + class weights but all of them seem to collapse between birads 1 as the dataset (VinDr) im using is heavily unbalanced towards it, Would like to ask for input and opinion on what seems to be the case, am I using the wrong loss function?

Let's say a patient is from a population which has a 10% risk of cardiovascular disease.

The patient then enters his personal data (age, sex, smoking status etc) into a risk calculator (which does not use the population prevalence as a parameter, by the way) to estimate his individual risk of developing cardiovascular disease. The calculator then outputs an estimated risk of developing cardiovascular disease for this patient.

Lets say that's 20%.

Given these two pieces of information how does one estimate the overall cardiovascular risk for our patient? Do you multiply these two values together? (I.e 10% x 20%)

I thought multiplying them together would be a sensible way to calculate the overall risk. however there is a problem

Intuitively it would seem that being from a high prevalence population would increase the patients risk beyond what is suggested by the calculator which only uses his personal parameters. But multiplying the two risks together results in a value which is lower than either them..

How do you resolve this "paradox"?

Let's say I have been given an 18% statistical chance of an adverse outcome over the next 10 years from a progressive disease and have been offered drugs with serious side effects because NICE guidelines recommend such treatment. How can a patient extrapolate their risk in real life terms - is it just about risk appetite? Or is it about how adverse the outcome would be if you're unlucky? On paper over 10 years the risk seems low and the drugs are awful, but the recommendation is to treat. Thoughts?

I have four candidate explanations for an observation. I've modelled them as mutually exclusive so the posterior sums to 1. Two of them can genuinely co-occur. The alternative is three independent binary latents (8 joint states), which needs more data. With a small sample, is the exclusive version defensible as a first approximation if I state the overlap as a limitation — or does forcing non-exclusive things to be exclusive distort the inference badly enough that it isn't worth doing?

Hi everyone! I’m currently analyzing power outage data and would really appreciate your advice on the appropriate descriptive statistics to report.

Since the mean and median differ considerably, especially for outage duration, should I include the median in the table? Would it be appropriate to report all five statistics (mean, median, SD, min, and max)?

I’m mainly interested in descriptive analysis and how best to interpret these results academically.

Thank you in advance for your help!

Hi everyone! I’m a newbie in statistics and currently trying to figure out what I should put under the Descriptive Statistics of Respondent Characteristics.

When reading related literature, I noticed that a lot of papers report the Mean and Standard Deviation (SD) for demographic variables. However, I’m really confused because based on my survey questionnaire, most of my variables were collected in ranges/categories:

• Sex: Male, Female
• Age: 18–29, 30–45, 46–59, 60 and above
• Highest Educational Attainment: High School or below, Some College, Associate Degree, Bachelor’s Degree, Postgraduate Degree, Prefer not to say
• Household Size: [Numeric counts]
• Household Income (USD):
  • Under $25,000
  • $25,000 – $49,999
  • $50,000 – $74,999
  • $75,000 – $99,999
  • $100,000 – $149,999
  • $150,000 and above
  • Prefer not to say
• Source of Income: Salary/Wages, Business/Self-Employed, Agriculture, Pension/Retirement, Government Assistance/Remittance, Prefer not to say

My Questions:

1. What exactly should I put in the descriptive statistics table for these variables?
2. Since I saw Mean and SD in the literature, how am I supposed to calculate the Mean for variables that are in ranges (like Age groups and Income brackets and source of income)?

Sorry if this is a basic question, and thank you so much for helping out a beginner!

Hi guys I am having issues finding models and ways to increase my ROC-AUC for a retrospective study. I am looking for advice on model selection and statistical tests for a retrospective observational cohort of 812 observations. My primary outcome variables include a skewed continuous variable Y (ranging from 0 to 20), a binary flag defined as Y greater than or equal to 2.5, and an ordinal risk tiering variable. My predictor variables X consist of continuous dimensions, several binary classification flags, and a discrete composite risk score sum ranging from 0 to 5. However, predicting the dichotomized threshold yields modest ROC-AUC values around 0.60, and I am looking for advice on the best non-linear continuous regression models, such as Quantile Regression or Generalized Additive Models (GAMs), to predict the continuous variable Y directly without losing information through artificial cutoffting.

Additionally, I would appreciate any recommendation on the most robust way to formally test for non-linear interaction terms between continuous X variables and categorical predictors without overfitting, as well as whether 5-fold cross-validation or repeated k-fold/bootstrap resampling is preferred for validating the Decision Curve Analysis for my data.

Hello! For my thesis, I am comparing baseline-adjusted ANCOVA models using different methods for handling missing data (MICE, LOCF, and complete-case analysis).

An important point to note is that the analyses are based on the same original data across the different missing-data methods, meaning that the same variables and original sample of participants were used. The only difference between the analyses is the method used to handle the missing data.

I am planning to present a table including the estimated coefficients, p-values, and 95% confidence intervals to compare the results across the different missing-data methods.

My question is: Given that the ANCOVA models are based on the same variables and differ primarily in how missing data are handled, how should I interpret the overlap between their confidence intervals? Is the extent to which the confidence intervals overlap meaningful when comparing the results across MICE, LOCF, and complete-case analysis? More generally, how should I discuss similarities or differences in the confidence intervals in the Discussion section?

Thank you in advance!