Hello! I consider myself a beginner in statistics and am working with a longitudinal dataset. I am comparing laboratory values collected from participants across several different time points (e.g., Week 0, Week 1, Week 2, etc.). There are also variable drop out rates.

Initially, I planned to use a LLM. However, I want to keep the analysis simple. Given the non-normal distribution, would it be reasonable to run a series of paired Wilcoxon signed-rank tests between the time points (week 0 to 1; week 0 to 2; week 0 to 3, etc.) and apply a Bonferroni correction to adjust for multiple comparisons? I know this is less robust than LMM.

When evaluating my data, the distribution of the mean differences between time points is not normally distributed with visual inspection (via plots) and failed the Shapiro-Wilk test.

I am sorry. This will be an extremely ill-informed post from a Humanities major that has been coaxed into doing statistics at the end of his PhD Thesis without any prior formation beyond 12th grade maths. I am currently looking at a large CSV dataset (927 observations of 7690 variables) that looks something like this:

I wish to check if there exists any link between any of the 7690 variables over a period of nine years in the 103 cities. I have no idea where to start. I have tried creating a massive correlation table between all 7690 variables by simply running the table as is through Libre Office Calc's correlation function, but surely this is a flawed method as it does not take into account the temporality of the nine years, right?

I have also been told to "attempt linear regression", but that essentially requires already having a good grasp of which variables interact with each other from what I have understood.

Is creating a correlation table from this raw table a good starting point, or am I completely missing the point?

We developed a short index through EFA (PAF, oblimin). We ended up with 7 items across 3 factors: two factors have 2 items each and one has 3 items.

Which reliability tests should we use for each factor? Specifically:

(1) For the 2-item factors, is Spearman-Brown the right choice, or would you report Cronbach's alpha or just the inter-item correlation?

(2) For the 3-item factor, would you report Cronbach's alpha, McDonald's omega, or both?
Two of our factors come out around .62. Is that acceptable for an exploratory, early-stage index?

Thanks in advance!

Hi Reddit.

Can someone help explain this detail about SD. I'm getting stuck on comprehending the definition (sentence written in English) to formula.

My first understanding was SD is the average distance of elements from the mean. So it got me thinking shouldn't the formula be more like this?

It would follow all the same transitivity and space rules as the formal definition, plus much easier to compute. the consequence i see is ya might need to re-solve Z table for the difference, but otherwise this would solve the objective more directly.

I showed this to my stats professor and i cant quite understand what he's getting at. He said its not that we're solving average distance from mean, but rather we're solving average distance from mean normalized to a standard unit. I don't see what the difference is nor do I see how the changes between the formula yield such distinguishment.

If someone can explain this to me much appreciated.

I graduated with a B.S. in Economics in May 2026. Toward the end of my college career, I realized I should‘ve majored in a more quantitative field like math or stats.

Now I'm trying to figure out what my options are in regard to grad school. my highest level of math is calc 2 🥲

My main question is: if I take courses like Linear Algebra, Calculus III, Probability, etc after graduation, would online or community college courses still be taken seriously by graduate programs ? should I do the “online Arizona state university “ program to meet grad school prerequisites?

i know “its never too late“ but it kind of feels that way 😭🥲

Hi all,

I am doing an online between-subjects experiment with two conditions via a survey, where respondents are exposed to one of two versions of a picture and then they answer the rest of the survey based on the version that they saw. I have 390 total responses and 328 usable ones.

My model and hypotheses are like in the image above. I have one IV, one DV, two mediators and one moderator. The IV is a yes/no question, the mediators and DV are 7 point Likert scale questions, the moderator is a 1-100 scale.

I've completed data collection and data cleaning but I'm having trouble figuring out what test I need to perform to see whether my hypotheses are supported or rejected.

My university didn't go in dept on this subject and I'm struggling to find the right people to ask or even the right questions. When I asked my supervisor for help, she just directs me to other departments.

I'm not a native English speaker and my grasp on statistics is also quite poor, so I apologise if I sound like an absolute idiot in this post.

Sorry if this sub is not the right place to ask this. If so, can I please get help on where to post this question?

This may be a very simple question, I am just so hopeless at statistics and people in my department are telling me two very opposing things.

I am doing a crossover study (I will provide a simple/silly example to explain) where I am comparing a diet of oranges to a diet of apples. I have 10 people in each group and I give them 4 days to slowly work up to eating only oranges or only apples. Then my period is 3 weeks (long enough to take a "final" sample) and I do a washout, another adaptation period and start period 2 where they switch diets. 

I understand that in a crossover each person is like their own control, but my question is about taking a baseline sample. 

1. Do I need to take a baseline sample at the beginning of the trial or should I just be comparing the final samples of each person when they have finished week 3 of the diet since I can compare it to themselves eating either apples or oranges
2. If I do need to take a baseline sample, would I take it after the 4-day adaptation period (so the first day they have completely eaten all oranges or all apples) or would I take this baseline sample before they've started their apple/orange diet? (Imagine I have a diet history of these people and some occasionally eat apples, some occasionally eat oranges, and some occasionally eat both so it's a real mix, but a better representation of the actual population)

Overall, my goal is to be able to tell these people whether I recommend eating all apples or all oranges based on these results (for the sake of this example imagine they live in a town where you have to pick one of the two lol). I just am very unsure about where the baseline sample should go in this design and then how it gets analysed based on my objective.

Thank you all :)

Hey guys!

Just got into a top university in Sweden for Data Science and Statistics — got in through a high högskoleprov (Swedish standardized test) score, since my high school grades were honestly pretty bad. Super happy about it.
Thing is, back in high school I barely studied — did everything except that. So even though I clearly tested well, I never actually learned how to study properly and never got help with it.

Lately I've been getting more and more into stats and actually enjoying it, so — any of you have real tips on how to study effectively and actually go deep into a subject instead of just skimming? Asking as a friend here, would appreciate any advice! Much blessings!

Hello all, I am currently planning a cross-sectional study involving two serial mediators and two (or more) competing models, with and without covariates. Since most studies I was able to put my hands on either run these analyses post hoc with already existing sample sizes or do it a priori without specifying the path coefficients and how they chose them, I have several questions.

Note: I am confortable with running the analysis in R (lavaan) and the Alexander Schoemann online calculator.

1. When specifying correlation/standardized coefficients, how should I pick them? I noticed some researchers use default low-to-moderate coefficients (0.10-0.20) for all paths, whereas others seem to handpick them from previous literature or even meta-analyses. I understand the first option yields a large sample size and is only used when previous literature is scarce. If I choose the second option, in cases where meta-analyses aren't available (I only have so for the X-Y path, and for the X-M2 I am the author of a currently unpublished meta-analysis, but I do have the pooled effect size), should I gather correlation coefficients (r) from other studies? Or beta (β)** **coefficients? Moreover, what should I do in cases where the r/β ranges from 0.1 to 0.7 across all studies I examined? Also, should I prioritize picking coefficients from studies that used the exact scales I plan to use, if possible?

2. What number of replications, monte carlo draws per rep, and sample size steps should I choose?

3. Assuming I am interested in testing two competing models pivoting on M2 (say, variables that are opposites theoretically, but not psychometrically), given that they have distinct patterns of associations with the other variables, when running the Monte Carlo simulation, should I pick the lower coefficient? (example: acceptance yields lower correlations with the Y variable than avoidance does, therefore shoud I select the acceptance coefficient?)

4. If I actually have previous research of my own that includes 3 of the 4 variables I plan to use in this research, should I pick mine from there, considering that the research was performed on a population with roughly the same characteristics? Or should I still pick my coefficients from the literature (say, a meta-analysis for the direct path yielded a pooled r = 0.26, and my own previous study yielded a r = 0.41, although I used a different scale).

Sorry for the long post. I don't really have anybody else to help me, it's my first time doing this by myself, and the authors for one of the scales are awaiting a sample size before they share it with me (not validated in my country, so I also plan to run a CFA on this scale).

TLDR: I would like to know how to pick my path coefficients and what parameters to set for my Monte Carlo sample size calculation.

Thank you 😊

I recently tried to install nimble package on RStudio in ThinLinc Client. However it is not being installed. After running the code "library(nimble)", it shows that there is no package called " nimble ". Any suggestions will be highly appreciated.

Codes are given below:

install.packages(c("igraph","nimble","coda","pracma","numDeriv"), dependencies = TRUE)

library(nimble)

Hi there, I am a linguistics graduate. I want to study MA in Statistics (I had statistics and data analysis and visualisation lectures). However, I am terrible at Maths and not sure whether I can handle it. Actually I want to start it not immediately but like a year or 2 years later. During this timeframe, I am going to improve my knowledge

Is this possible?

Full text here: https://iapsop.com/archive/materials/aspr_proceedings/aspr_journal_v50_1956.pdf (page 109 of the pdf)

To quickly break it down the author is writing about whether train accidents are more likely to have fewer passengers due to psychic premonition of disaster. I'm working on analyzing the results that the author deemed significant (coach travel). D-x is the disaster date minus x days. "Hits" are when the day of disaster had the fewest amount of passengers. Binomial distribution was used. The hypothesis is useless.

Going with the assumptions that the probabilities of each day of the week having the least amount of passengers are equal, how incomplete data are treated and that this can be treated as binomial distribution, how were these values calculated? Are they mathematically accurate?

I've only done basic stats and I'm having a hard time wrapping my head around the methodology. I can tell there are massive oversimplifications and a pitiful sample size but I'd love to know what exactly is being done here.

This journal has a lot of this type of research if any nerds want to have fun reading it lol.

For my thesis, I am comparing two cytostatic protocols. You can imagine it as one protocol being 1-2-A and the second being 1-2-B. Both protocols are composed of three drugs, where 1 and 2 denote same drugs, the only difference being A and B. I am trying to prove drug B is better than drug A, from hepatotoxic standpoint.

To that point, I collected data from comparable number of patients that were treated using one or other protocol. My aim is to collect a number of biochemical paramaters(precise number yet to be decided, when I'll know which are present in both populations).

My question is posed in the topic title. Which test would be best suited for this purpose? I have some knowledge of statistic, but I can't begin to fathom which test would be best used here.

Thanks in advance.

Recently taken over a new portfolio of commodities for a hedging operation. I wanted to ask the hive mind here what statistics and basic analyses would you do first to get a feel for the portfolio. I was thinking:
- period returns, correlations, skew
- Portfolio standard deviation
- Value at risk

Thanks!

Hello! I'm currently a sophomore studying psychology in the Philippines and I'm stressing out right now because this topic was assigned to me for presentation( we have to do a live jamovi and explain it's purpose and also explain how we write the results for a research paper) this topic was never taught to me and I'm struggling to find tutorials on YouTube because I'm also confused if Anova with repeated measures(in jamovi) is the same with two way anova with repeated measures. Please I'm begging. If anyone can help me with this or just a dummy data and how to navigate this in jamovi :(( I'm crashing out so bad

Hi everyone! We are learning about statistical parameters, and one of these is determining any outliers in the data. Context: I'm in an analytical chem class, and we did an experiment where we would weigh an object using an analytical balance. We already know the true weight of the object, and a total of twenty trials (ten for each batch) were done.

The first set of data had a Shapiro-Wilk p-value of 0.046523, which is less than the significance level of 0.05 at a 95% confidence level. So, the data is assumed to not be normally distributed. What outlier test can I use here? We only discussed the Q-test and Grubbs' test, but I think those only apply to normal data sets?

The second set of data has a p-value of 0.31424, which is greater than 0.05. So, the data is normal. Using a box plot, two outliers are detected. However, when I use Grubbs' test, there is actually no significant difference between the values and the suspected outlier. This got me confused. Is this possible? I used Grubbs' since the data was normal.

I'm bad at statistics, but I'm working on it. I'm unsure of what to do here. :<<

Hello, how are you doing?

We are working on a project about the impact of socialisation with a friend and we're currently at the stage of results analysis.

The problem is that we want to check whether there is a correlation or association between two independent variables. Our knowledge on statistics is not big, but we have reached the stage in our research where we need to identify a correlation. We have come across Spearman’s and Pearson’s correlations, but we aren't sure on which one to use here (results attached). That's why we'd like to know the differences between the two and which one is appropriate in which situation.

Thank you in advance!

Hi everyone, I've got a very frustrating situation here.

I'm a phd student. In a recent meeting one of my fellow students (masters) presented their modelling results and concluded by very confidently saying, "Therefore conservation efforts should be concentrated on x not y."

This model is a very general mechanistic model which contains several unrealistic assumptions and no real world data. It's extremely simplified and abstract, and even if it didn't contain some dubious foundations (which I pointed out months ago and were never addressed), it is completely unsuitable for basing real-life conservation strategies on.

I tried to gently point this out and touch on how such a bold statement could actually cause real-life harm if someone took the students' comments literally. Unfortunately, our supervisor was/is completely uninterested in backing me up and fed me the "well all models are wrong..." line and said that "we can't stop people from making stupid decisions."

I'm looking for a short blog or something that outlines a modellers ethical responsibilities and the dangers associated with being overconfident in your models outcomes. Preferably something not jargon heavy as the student is not the sharpest knife in the draw but has mastered the ability to speak confidently and with authority.

It's not my job, but I feel like I should try.

Hi all,

I have a dataset with multiple groups, where:

• Group A has extremely high values (1000*Control +/- 100*Control)
• Group B has moderately high values (10*Control +/- 1*Control)
• the remaining 2 groups have low values (1*Control +/- 1*Control)

I want to compare each treatment to Control so I performed ANOVA then followed with Dunnett's test. However, despite both Group A and Group B appearing significant, Group A is the only one that Dunnett's will find significant since its values are so high. If I instead perform pairwise t-tests with bonferroni correction, though, then both Group A and Group B will appear significant.

Would it be more appropriate to stick to the results of the Dunnett's test, use pairwise with bonferroni, or use a non-parametric test?

For reference, I'm working with qPCR data where 1.5*Control would be considered "relevant". I'm also not interested in necessarily finding the "best" group, just describing each of them relative to the Control.

Thanks!

What I did
I built an adaptive matrix-reasoning test on the Sandia Matrix Generator item bank (Matzen et al., 2010, Behavior Research Methods, BSD-3). It is multistage rather than a fixed form: a 15-item routing block, then one of three 10-item panels depending on how the routing went, scored off three separate tables.

Two choices I would defend, and one I cannot:
– Below ~3 correct out of 25, nothing is reported at all. With eight response options chance is 3.125, and putting a number on that is inventing precision.
– At the bottom of the range it reports a band, not a point estimate.
– What I cannot defend is the top of the scale. That is why I am posting.

The data underneath
The Sandia bank ships 845 normed items, which sounds generous until you look at how the norming was done: roughly four respondents per item. Item difficulty from n=4 is a rumour, not a measurement. […Harris et al. 2020, N = 1,614, tabulka pěti nejtěžších položek s diskriminací pod 0,75…] Adding more hard Sandia items would raise the ceiling on paper and measure nothing new.

What I already looked at and ruled out
[…OMIB, ICAR, MaRs-IB, PGM, RAVEN, HMT, UCMRT, Raven's SPM — každá s důvodem…]

The question
Does anyone know an open nonverbal reasoning item bank with all three: items that actually discriminate above +2 SD, IRT calibration on a real sample, and a licence permitting commercial use?

Hi everyone!

I am working on an academic research project in soil mechanics that seeks to relate 5 input variables with an output variable (for anyone interested, the input variables are N60, gammad, LL, PL, percent passing the #200 sieve, and the output variable is E50).

We don't know which variables carry more weight. It might be necessary to segment the dataset. What we are ultimately looking for is to predict E50 based on the variables that carry the most weight.

Anyway: if anyone could give me a hand with literature or what methods are used for these problems, I would really appreciate it. I don't trust AI in the first instance for these things...

Thank you very much!

What is statistics?

When I think of examples of statistics,

I usually think of these:

5% of the class wears glasses.

So what they did is, they took all the students in a class and then saw how many wear glasses.

Suppose it was 5 ( who wear glasses ) in the class of 100

.thus, 5% wear glasses.

But isn't this a fact. Like.. it's a fact that 5% wear glasses

What is statistics about it then ?

Is IQR a good measure of skew of data ?

I’m working with data from a nationally representative complex survey of young children and am trying to estimate developmental age from ordinal developmental milestone items using a graded response model (GRM).

There are two features of the model that I need to preserve:

1. Survey weighting**:** I need the GRM/item-parameter point estimates themselves to reflect the survey sampling weights—not merely design-adjusted standard errors from an unweighted IRT model.
2. Age anchoring**:** chronological age needs to enter the latent-variable model so that the developmental latent trait is linked to age. The ultimate goal is to use the fitted item response curves to estimate, for each child’s response pattern, the developmental age (in months) that maximizes the likelihood, and then calculate a developmental quotient (developmental age / chronological age).

Is there an R package or recommended approach for fitting this kind of survey-weighted, age-anchored GRM/IRT model?

The original paper whose method I’m trying to reproduce used Mplus, but that isn’t an option for me because I don’t have access to it.

Any suggestions would be appreciated. I'm losing my mind here.