Hi everyone,

I'm running behavioral trials on Pachycondyla ants as a side project alongside my main research (which focuses on ant venom biochemistry), but I wanted to look at when/how the venom actually gets used in different contexts. Problem is I have zero background in ecology or ethology, so I'd love some guidance from people with an ecology/ethology background cause I want to do this properly.

I'm running behavioral trials with Pachycondyla ants confronted with 6 different opponent ant species, across 3 different experimental setups. Each trial is filmed for 1.5–2 minutes (duration varies slightly between videos), so I know I need to normalize for time somehow. And each individual (from both side) is only ever used once so no ant gets reused across trials. For each video, I count occurrences of 5 behaviors:

• Antennal contact (antennae touch any part of the opponent's body
• Mandible opening (mandibles open, with the two individuals about one body length apart)
• Biting
• Gaster flexing (abdomen curved as if about to sting)
• Stinging

 What statistical approach would you recommend for count data like this? What kind of plots actually work well for this kind of data? not looking to just dump a boxplot of raw counts if there's something better... I've already gone through some papers on ant behavior / interspecific aggression, but if you have specific methodological references you think are worth checking that use a similar design, I'd love pointers.

Not trying to overthink this, just want something defensible since it'll probably end up as a supplementary part of a bigger paper!

Thanks in advance :)

Hello, I am having trouble finding the marginals option using the scatr module. I have downloaded it and followed the instructions that my professor provided but it seems that the Marginal option does not pop up after I input my x and y axis.

Anyone know how to access that option?

Hi I am a master’s student and I have a rather basic understanding of linear mixed effects models. I am helping with statistics in a study which has 2x2x10 mixed design (2 between and 10 within). I am confused as to how am I supposed to start modelling. Do I do it according to the hypothesis so I all of our IVs in the model and only then check whether the random slopes/ intercepts are needed, or do specify the random model and only then look at the fixed one and add the predictors one by one and see how the model is. Can someone please help me understand?

Im working on a research paper and my professor suggested I display significance on our bar graphs using letters (title is very much so a place holder lol)

I've never done this before so Im wondering if im correctly assigning the values to each site. Below are the significance calculations.

Ive been using this video: How to Denote Significant Differences in Tables and Graphs to help me learn but im concerned im not quite getting it.

My line of thinking with how I have it layed out is: Shadyside is not different than the others so it gets "a", Basin is significantly different than the Mosque so it gets "b", Leisure is significantly different than all the others so it gets "c", and the Mosque is not significantly different than any so it gets all the values.

Any help would be greatly appreciated! Either correcting my work or pointing me in the direction of a source I can use to understand better!

Hi everyone! I'm currently working on my bachelor's thesis and struggling my way through the statistics part. I feel like this is probably a simple question for someone who knows what they're doing but unfortunately that's not me.

First of all, my H2 is: "Perceived destination relevance correlates positively with the willingness to travel to a concert."

The plan was to test this by using a Pearson correlation in SPSS. My professor approved both my hypotheses and my questionnaire before I published my survey, so I figured it would be smooth sailing from here. (Nope.)

• For destination relevance, I had five Likert items with acceptable Cronbach's alpha, so creating a quasi-metric/composite score was fairly simple.
• For willingness to travel, I originally had four Likert items. However, one item clearly reduced Cronbach's alpha, so I removed it. Now I'm left with only three items. Those three show an acceptable internal consistency though (Cronbach's alpha = .880).

This is where I got stuck. From what I've read online, it's my understanding that it's a rule of thumb to use at least five Likert items to combine into a composite (or quasi-metric) scale, which I will need if I want to use Pearson's correlation. Since I'm not very experienced with statistics, I'm honestly not sure what the correct approach is anymore.

Which brings me to my questions.

Is it acceptable to create a quasi-metric score from only three Likert items if they're supposed to measure the same construct?

And if not, what would be the appropriate way to test my hypothesis instead?

I'm happy to provide the items themselves, SPSS output or any other information if needed. I'd really appreciate any advice or help because I've been going around in circles trying to figure this out by myself. Thanks!

Hi, I have recently completed my master's in financial economics, and I am looking to do another master's in statistics. My bachelor's was in business, really not a quantitative major, and I am worried about the admission requirements.

Do you think that the math and stats that I learned below in my master is enough to convince admissions that I have sufficient background knowledge to follow an MS in Statistics? (My targeted programme is at the National University of Singapore)

My quant courses covered:

• Linear algebra: matrix representation of systems of equations, determinants, inverse matrices
• Optimization: constrained and unconstrained maximization/minimization of multivariable functions using Lagrange multipliers
• Calculus: chain rule, slope of level curves, homogeneous functions
• Inferential statistics: confidence intervals, hypothesis testing
• Comparing means across groups (independent samples t test, paired sample t test, one-way ANOVA)
• Analysing categorical relationships (chi-square test)

And then it's been mainly econometrics and quant finance:

• Time series (cointegration, VAR/VECM modelling, unit roots)
• Stochastic models and probability theory
• Arbitrage pricing theory and risk-neutral valuation

Also, for programming, I have used mainly R and EViews, and I am working to learn Python.

Thanks a lot.

Bookmaker odds (and prediction markets) imply probabilities that sum to more than 1 because of an embedded margin. I want the latent probabilities behind the distortion. A few things I can't resolve cleanly.

1. Model choice for removing the margin. Proportional normalization, Shin (a latent proportion z of informed traders), and the power/log method impose different unobservable structures and give materially different estimates on short prices, enough to flip the sign of a downstream signal. Since you never observe the true p, only realized 0/1 outcomes and a later sharper price, is there a principled basis to discriminate between these models, or is it identifiability-limited and I should just report sensitivity across all three?
   
2. Pooling under a missing low-bias reference. I anchor to one near-efficient source when available; when it's absent I take the median of the other sources' de-vigged probabilities. But those sources are strongly correlated (several are effectively clones), so the median behaves like a median of correlated estimators: it looks precise while carrying little independent information. How would you estimate an effective number of independent sources and down-weight accordingly, and is abstaining the more defensible choice when the low-bias anchor is gone?
   
3. Combining a trusted low-variance estimator with a correlated ensemble. When the reference IS present, precision-weighting it against the consensus assuming independence is clearly wrong. Is there a clean correlation-aware pooling or shrinkage approach for one low-variance source plus many correlated higher-variance ones?
   
4. Validation target. I grade earlier estimates against the closing price (a later, sharper estimate), not realized outcomes. Under a proper scoring rule, is "tracks the later estimator" a coherent target, or does it conflate calibration with just chasing a second estimate? And what does the selection bias look like when you only get a validation point on markets that reach a close?

(Aside that turned out to matter: my reference source silently dropped out of my data feed for months and the pipeline substituted the fallback the whole time while still labeling outputs "reference-anchored." The values populated fine, so nothing looked wrong. I only caught it after storing a per-observation flag for whether the reference actually contributed. Log provenance, not just values.)

Dear Experts of Statistics, I am a slightly confused student here with a doubt. As a part of my Masters dissertation, I created a psychological questionnaire. Using Jamovi, I conducted an Exploratory Factory Analysis using Jamovi.

Now some of my items are highly unique with uniqueness above 0.80. Does this necessarily mean that I must remove these items?

Based on review of literature and content validation, these items are highly theoretically relevant to my questionnaire and thus I am apprehensive to remove them. I could possibly reframe them right? Although I feel they already are in simple language. I would appreciate any insights.

The future plan is to collect more participants to do a Confirmatory Factor Analysis.

I want to understand statistics better for research purposes. I have found several (very good) textbooks on statistics in JASP. They show you what to do to perform a given test step-by-step, and they explain the theory behind it in-depth.

However, they all use very basic, neat, and clean examples. I have not been able to find good resources to learn the more strategic side of statistics: when and why to use a given test, a given correction, or a given adjustment; how to best approach testing a given hypothesis, and so forth.

For example, I know how to run a multiple regression in JASP, but I want to learn the best practices for selecting predictors, applying valid corrections, interpreting the results, etc. I know how to run a mediation analysis, but how to interpret a situation where, say, only the B path is statistically significant?

In other words, I want to learn not only how to go through the motions in the JASP interface, but what to do, when, and why. Are there any good resources for that, short of attending university-level courses? (Free) online courses would be welcome as well.

Hi, im working on developing a board game and need help determining the formula to use for statistics analysis. I have four 6-sided dice. Each face of the die either is or isnt a desired roll. If I were to roll a combibation of dice (e.g., 2A & 1B) what would be the probability formula that i would roll *at least* 1 and then what would be the probability formula of me rolling a certain number of 1s. Aka if I roll 5 dice of varying types and want at least two 1's what is that probability. Sorry if this is the wrong subreddit to post it in. Statistics was always my weakest subject in college and my brain started hurting trying to understand this one thanks again!

Die A: 1 0 0 0 0 0

Die B: 1 1 0 0 0 0

Die C: 1 1 1 0 0 0

Die D: 1 1 1 1 0 0

I need to estimate the proportion of Y given covariates X and Z. I could bucket the covariates and then just calculate the proportion Y for every fixed X,Z pair, but not every X,Z pair has enough datapoints for the variance to be acceptable.

Thankfully, I expect and can observe a clear relationship between Y and X,Z. Fitting a logistic gam/tensor product to the data looks "much better" but how do I quantify this "much better"?

If I fix X,Z and just count the proportion Y, I know the underlying relationship is as bernoulli distribution, I know my estimator is unbiased, and I can just read the variance formula off of wikipedia and construct confidence intervals to quantify "how correct" the predictions ought to be given my sample size.

But a tensor product/logistic gam is for sure a biased estimator (I believe some curvature exists, but the smoothing penalty has no way of knowing what curvature is real and punishes them all equivalently). Splines and 2nd derivative smooths have nice behavior but at the end of the day they're kind of arbitrary assumptions I'm making about the underlying behavior of the data that doesn't have to be true at all. How would I construct the equivalent of a confidence interval? Can I make quantifiable statements about how good the predictions coming out of my model are?

(I understand that confidence intervals don't measure how good any individual prediction or model is, but rather, measure how good the process is that we used to generate the model).

Hello, I am interested in applying for a PhD program in Statistics. However, I feel my current background needs strengthening, as the field is quite broad. Could you please recommend any courses or textbooks that would help me prepare?

I’m trying to decide between 2 sets of adjusters in a cox regression with an interaction term. There is theoretical reasons to include and not include a particular 3 adjusters. It’s to do with child development, I’m interested in 2x specific domains of development on the outcome, but my supervisor wants to know if my results are picking up the other domains which I wasn’t adjusting for. When I add the other 3 domains into the model, the hazards actually increase. Suggesting to me that my theory is correct, that the 2 domains I’m interested in matter the most. But my supervisor says that including the other domains shows that in fact, when taking into account the child’s full developmental picture, the 2 main domains matter even more and therefore I should include them.

So I’m trying to find a way to assess which model is the best. I don’t know if it matters if it’s by testing fit or prediction performance. From what I can gather (psych/public heath/epi PhD, not a stats PhD) the AIC and BIC are good for this task, but when I ran them, they’re inconclusive as far as I can tell. Is there another test I could do? Or is this now a case of pick one and justify it, move on and wait for peer-review feedback 😅

Hello everyone,

I've conducted a study a few years ago where I've sampled individuals of a species X in different plots for a couple of days. However, these plots are close together and I cannot be certain that all of the individuals that I've sampled are different. To do the statistic analyses, every plot is a sample. What would you do in this situation to account for the possibility of repeated measures and lack of independence?

Thank you so much!

Hey there stats friends,

I’ve recently submitted a paper and received reviewer feedback that I don’t fully understand. I’m hoping someone here could shed some light.

I have a retrospective case series examining outcomes of a repair technique for a particular fracture in dogs. As part of the study, I measured radiographic joint angles in the same 28 cases at three time points: preoperatively, immediately postoperatively and at follow-up.

I used paired t-tests for three prespecified comparisons: preoperative versus postoperative, postoperative versus follow-up, and preoperative versus follow-up.

The reviewer suggested that I should first perform an omnibus test, such as a repeated-measures ANOVA, and then perform the paired comparisons as post-hoc tests.

I don’t understand what the omnibus test would add when these three pairwise comparisons are the scientific questions of interest. Is the concern simply multiplicity? If so, would applying a multiplicity correction such as Holm’s method to the paired tests be sufficient, without requiring a significant omnibus test first? Or does the omnibus test have some advantage that I'm not yet understanding?

Thanks :)

I’ve been using bland-Altman plots and intraclass correlation coefficients to see how consistent the scoring of the same wearable device is at different wear locations.

Some context: The two devices are the exact same and capture the same data and are worn at the same time. Let’s say 10 people wear both devices at the same time for 7-14 days, and I want to look at both agreement and consistency of the features that are recorded that are captured at a day-level. So essentially I have one score produced per day for each device for each person.

I’m struggling to understand what the most plausible way to evaluate this is, as not all participants have used the devices for the same number of days, should I take an aggregate score across all of the users days wearing it and use that for comparing device scores at one reading per participant or is it possible to use multiple time points in the evaluation and have more datapoints for the ICC?

Perhaps there’s a better way to evaluate this that I haven’t considered? Sorry if it’s a rookie question, pretty new to stats!

Prepping for an exam for a grad class on linear models. Other than the matrix-vector equation representing these different models, how else can we describe and differentiate these models?

Hello, I'm a maths teacher in Scotland reviewing some old exam papers. In one where a Pearsons test is found significant, it is stated that

There is a 95% certainty that the result will not have occurred by chance

I think I know what is wrong what that statement, but would be very grateful if someone could help me by explaining what the error is.

Doing a secondary analysis on a dataset looking at the effect of medication type and timing on weight. N=216 with individuals randomized into 2 groups (immediate vs delayed medication administration).

I have several outcomes measured including weight, blood pressure, blood glucose etc and want to compare them to baseline. My current hypothesis is that a certain medication type administered immediately results in weight gain.

Would a Two-Way ANOVA be a good test for this question? I was thinking a paired t-test wouldn’t be ideal because I’m looking at two different conditions (medication type & time of administration here)

I graduated from college in 2021 with a degree from a small university, in Business. I had taken 2 stats classes and done well, but was still very much a beginner. I started working in tech support and got a job as a data analyst, and was working enough with Data Scientists that I wanted to learn stats to level up and do work like them.

I got into a handful of programs but ended up starting a remote Stats masters at a Top 25 program in 2024.

The first semester was really busy but I felt like I was behind, but learning. At the end of December that year, I lost my job and my mom died. I was in an extremely dark place, but didn’t want to drop out, so started leaning on AI to get me through school.

I should have put my degree on hold, I should have dropped out, etc etc. I know, and I am not interested in comments telling me about how big of a mistake I made. I am trying to move on!

I graduate this upcoming spring (1 class per semester). I am planning to hit the books and never use AI during these last semesters, but now that I am at the end of my program, I feel so behind. I don’t really know if I learned anything. I have gotten out of that dark place but I feel like a failure for essentially wasting my time as a masters student.

I want to learn stats! I am familiar with concepts but so behind. I’m basically still a beginner. So I am humbly coming here to ask - what books, courses, articles, projects, Udemy, masterclass, DataCamp, textbooks, fun books, etc. have helped you learn and understand stats the best? I am committing to putting in the work but I don’t know how to teach myself and I’m afraid I’m gonna graduate without any knowledge. Please share what has helped you! What projects have you done that have cemented concepts?

I am desperate and sad but am trying to start over. I have 2 more semesters that I will work so hard on but I need to get a baseline as well.

Thank you for understanding! Thank you for your time!

Hello!

I’m student reading a scientific journal article. It’s a meta-analysis that has studies using Tau-U and Cohen’s D. What is the difference between the two and why would you use one vs the other?

My goal is to be admitted to a PhD program in theoretical statistics. Would pure math research in areas distant from statistics (like number theory or algebra) be less attractive to the admissions committee compared to a direct research experience in fields of statistics? (Like Bayesian or high-dimensional statistics).

Hi, everyone! I'm validating a questionnaire for my thesis, and I'm following the conceptual model and measurement scale from a published paper. Because of that, I'm planning to perform a Confirmatory Factor Analysis (CFA) rather than an exploratory analysis.

One of my constructs is Economic Barriers, measured with 3 items.

• Item 1: Organic products are too expensive.
• Item 2: The price of organic products is an obstacle to buying them.
• Item 3 (reverse-coded): Even if organic products are more expensive, people should still buy them.

All items use a 1–7 Likert scale.

After reverse coding Item 3, my Cronbach's alpha becomes quite low. Looking at the responses, many participants strongly agreed with Item 3 (high scores before reverse coding), suggesting that they believe organic products should still be purchased despite their higher price.

This makes me wonder whether the third item is really measuring economic barriers, or whether it's capturing something different, such as a moral belief, personal values, or purchase intention.

The original study reported a Cronbach's alpha above 0.70 for this construct, so I'm trying to understand why my results differ.

Since I'm following the original theoretical model, I have a few questions:

1. Is it common for reverse-coded items to perform poorly in reliability analyses?
2. Could this item be measuring a different latent construct even though it belongs to the original scale?
3. If the item shows a low standardized loading or poor fit in CFA, is it methodologically acceptable to remove it, even though it was retained in the original study?
4. Would reviewers generally expect me to keep the original scale intact, or is it acceptable to justify removing an item if the CFA supports that decision?

I'd really appreciate any insights from people who have worked with CFA, scale validation, or reverse-coded items.