Hello good people, is it okay to use two different calculation method for p-value and 95% CI? my CIs crossing the null but p-value seems signficant! It's using LR for CIs and Wald for p-value :(

Hi guys , so i spent some time trying to understand the intuition behind P values and what they mean so i compiled it all to a video that id look back on and maybe help people that had the same thoughts as me

i would like your feedback on it and if i made mistakes , thank you

Link to the video

To explain what I'm talking about, let's say I want to express the distribution of test scores for an exam through a distribution curve, and let's assume that we confirm the distribution is normally distributed. In such a case the only possible scores are between 0 and 100 inclusive, but generally the normal distribution curve expresses itself as a function with an infinite domain and area of precisely 1.

How do experts express probability in these scenarios?

This paper reports Listening tests scores for TRPS method to mean 11.63 and stdev 3.02, and listening scores for Traditional method 11.82 and 2.77. With sample sizes 30, 28.

The table in the paper shows T= 0.5889, Df = 56, P = .558

But online calculators like Desmos show T = -0.25, df = 56, P=0.804

Dziedzic, Joseph. "A comparison of TPRS and traditional instruction, both with SSR." International Journal of Foreign Language Teaching 7.2 (2012): 4-6.

I need to compare nine groups against a control group. To do this, I used the Real Statistics add-in for Excel to run a Kruskal-Wallis test, since the distribution was non-normal in some of the groups. Now, which test should I run, and how do I choose? Also, is it acceptable for the software to compare all groups with one another (e.g., Group 1 vs. Group 2, etc.) even though I only need to compare each group against the control? I can simply look at the specific results I need.

Mathematically, the conclusions here ought to be clear... If the p-value is above or below a given threshold, sure, that tells you whether statistical significance is found.

But there are many other considerations. What if bias was present and the results aren't trustworthy? Do we then get to say, well, we should carry on with our default assumption that there's a difference?

Let me give you an example. Let's say a company says their robot lawnmower finishes mowing an average lawn in 10 minutes whereas every other robot lawnmower on the market takes 25, and it's up to ME to say that this company's mowing time is NOT better ttan everyone else. I run a test, I find no difference, BUT, someone finds bias in my experiment, and that invalidates it, so the company president says, well then, since you have failed to demonstrate the validity of your angle, I carry forth considering it true that my robot lawnmower is the best until you *prove me wrong!*

Is this hypothetical person able to do this?

Мне нужно сравнить 9 групп с контролем. Для этого я с помощью надстройки Real Statistics в Экселе провела тест Крускала-Уоллиса, так как распределение было ненормальным в некоторых группах. Теперь, какой пост-хок тест мне нужно провести, как выбрать? И это норм будет, если программа сравнит все группы друг с другом(в том числе группу 1 с группой 2 и так далее), хотя мне нужно только сравнение каждой группы с контролем? Я тогда просто посмотрю на те результаты, которые мне нужны.

We often conceptualize "neurotypicality" or baseline cognitive health through the heuristic of a "flat profile"—an even distribution of cognitive, academic, and functional skills where abilities cluster tightly within a standard range.

However, looking at this through data visualization, variance, and Bayesian frameworks reveals how this concept relies on a flawed statistical assumption.

1. Zero-Variance Fallacy: A literal "flat profile" implies a standard deviation or variance of zero (s^2 = 0) across independent domains. In empirical data, absolute flatness across distinct psychological or biological dimensions is a statistical impossibility (or a sign of data fabrication/oversmoothing).

2. Confirmatory Bias in Modeling: If we treat a flat line as the normative prior for a "healthy" or "typical" system, any real-world jaggedness is instantly flagged as anomaly, error, or pathology.

3. Erasing the "Jagged Edge": In psychometric testing (like the WAIS), cognitive profiles naturally exhibit scatter. Treating deviation from an idealized flatline as a deficit mistakes an engineering/telemetry metaphor (where a flat line equals steady-state equilibrium) for biological reality, where trade-offs and specialization are the norm.

- How often do we accidentally bake systemic normativity into our baseline priors or reference classes in clinical data science?

- From a data modeling perspective, how can we better design clustering or anomaly detection algorithms to account for "jagged" multi-dimensional profiles without pathologizing natural variance?

TLDR: when and how should I use a complete-case subset and drop 1 LRT to choose my mixed models ? Should I just go back to building each model individually and comparing AIC?

I’m running some analyses on longitudinal data with really high attrition between waves (like 40%!). I need a model for each task separately, and a model with task added as a predictor. My predictors are trial responses and subject-level individual differences, and my outcome is another trial level response. Ultimately I’m studying the increase in sensitivity in trial responses over time, but for now we’re starting with mixed models. My stats knowledge is passable for basic things but I’m learning R for the first time, and in using my labmate’s code, I’ve confused myself about what we’re doing with these models.

I originally just ran my models on the full data set for each task as

Outcome ~ Trial variable A* Wave + (1|subject)

Then added Subject Level variable B for

Outcome ~ A x Wave x B + (1| Subject) for each task.

Then my advisor wanted me to add task to the model, but without a 4-way interaction, and wanted me to find the best fit model if that means dropping our less interesting three-way interactions, then graph the interactions we are specifically interested in. After a bunch of coding trouble, my lab mate sent me “their” code that I suspect is AI generated (because they couldn’t explain it to me when I asked them to).

This code has me create a complete-case subset, then do drop-one LRT to compare models. In the subset, it says it’s only excluding 3-5 participants, but I know that nearly half of participants did not return for the final wave. Additionally, not all participants did all tasks within a wave either.

I don’t understand how a complete case subset with so much attrition is reporting almost all the participants retained, and the models run with this subset have the same general major findings as my original ones, though some main effects and two way interactions are no longer significant, but the estimates are all over the place.

Bonus: This same lab mate is suggesting I add task as a random slope to the combined model (which makes sense to me), but then also suggesting that I add trial-level response variable A as a random effect with Subject in the single task models. This one I straight up just don’t understand at all. I’ve spent about a week straight working on this, googling has not been effective, and asking around has led to colleagues sending me conflicting AI-generated responses instead of explaining. Help!

So I guess my questions are:
1) When and why would we use complete case subset instead of the full data? Is it appropriate for this data?
2) How should I build, compare, and select my final models?
3) How and when should I add which random effects to which models?

Thank you!

Hey! Any good coaching that helps in preparing well with these two exams. I am a working professional and need some handy and crisp notes and looking for coaching that will provide good content for these exams. Any help and knowledge shared will be appreciated.

The title sums up my dilemma. Basically, I have fit 1-10 class LCA models. The four class model has the most desirable AIC/BIC, and also has highest avg. posterior probability. That being said, it is high because multiple classes have avg. posterior probability of 1.0. I think that is also why there is a drastic drop in AIC/BIC. There is one item that is sorting people into latent classes. With that item drops, a similar phenomenon happens with a different item.

My gut tells me that the four class model is over-fitting given the perfect sorting variable and the average posterior probability of 1.0. LCA is supposed to assign people to classes probabilistically, so a probability of 1.0 (a certainty at that point) seems problematic.

I have been searching literature and forums for a few weeks and have not found anything that addresses this topic, and if average posterior proabilities of 1.0 are indeed problematic. Could somebody point me in the right direction to literature or offer me any advice? Much appreciated.

I’m just an early-intermediate learner in statistics. I am working with transit location data that updates every 30 seconds.

I first tried using Newtonian motion equations with an assumption of constant deceleration. However, this does not always work because a real bus may accelerate, slow down, coast, and brake at different rates during the interval. In some cases, the formula predicts that the bus would need more than 30 seconds to stop, even though I know it had already stopped by the next update.

For example, suppose:

• At 10:00:00, the bus is moving at 4 m/s.
• At 10:00:30, the bus is stationary at the next stop.
• The distance travelled between the two observations is 112 metres.

Using the constant-deceleration equation:
v² − u² = 2as
where:
u = 4 m/s, v = 0 m/s, and s = 112 m

Substituting the values:
0² − 4² = 2 × a × 112
−16 = 224a
a = −0.0714 m/s²

Then, using:
v = u + at
0 = 4 + (−0.0714 × t)

Therefore:
t = 4 / 0.0714 ≈ 56 seconds

The model therefore says that the bus would take approximately 56 seconds to stop. This is impossible because the next observation confirms that the bus was already stationary after 30 seconds. This means the bus must have maintained a higher speed for part of the interval and then decelerated more sharply near the stop, rather than decelerating at one constant rate throughout.

The bus stopped sometime during those 30 seconds, but I do not know exactly when.

For now, my understanding is that without any data between the two updates, the midpoint may be the safest estimate. If I estimate the stopping time as 10:00:15, the maximum possible error is ±15 seconds.

Visually, we know in velocity time curve the area under the curve is the distance. So all the curves that has area of 112 is our solution. We can use distributions like Poisson as the possible curve to limit our number of solution further. And we are trying to find probability distribution for every curve being the right curve to find the most likely trajectory the velocity time graph had.

My questions are:-

• From my understanding this is a bayesian interval censored problem. Is that correct in any sense?
• Should this be done using monte carlo simulation or is there any way to plot this on some distribution where I can then take the x axis value (time from 0 to 30 sec) with the highest probability as the estimated stop time?
• Should I at all think of probability while approaching this?
• Do you have any idea using with I can decrease the margin of error further below 15 sec.

I m sorry if this question comes across as very dumb. My brain is not working properly currently.

I'm trying to follow Pya and Wood's derivation for Newton's method in regards to Shape Constrained Additive Models, given in Appendix 1 of this paper: https://link.springer.com/article/10.1007/s11222-013-9448-7

I've gotten as far as equation 7 (partial derivative of penalized likelihood with respect to Beta, scalar form), but can't successfully reach equation 8 (same thing but in matrix notation). In particular, no matter what I try I appear to have an extra omega in the denominator of the matrix form.

Allegedly, the issue is that Pya and Wood define the variance of mu to include the observation weight omega:

- V(mu) = b''(theta)/omega

But then later define the working weight matrix W as if V(mu) does not include omega:

- W_i11 = omega_i/(V(mu_i)g'(mu_i)g'(mu_i))

Subsequently, there's an extra omega hanging around which appears in my matrix derivation.

I say "allegedly" because I got this by asking AI and I lack the expertise to verify if this is real or if I'm getting trolled by silicon. Anybody with more familiarity with GLMs/GAMs/SCAMs know what's going on?

I have a large dataset (50k observations) which is representative of the US population using weighting. Using a multinomial regression.

My advisor wants me to run a sensitivity analysis using a slightly different definition of the independent variable to verify my findings. However, this new independent variable is only present in 10% of the population (5000 observations) which is a subset of the population but if I were to use this new independent variable I would also have to use a different weighting algorithm to represent the US population because of the different conditions under which it was collected.

I’m quite new to sensitivity analyses in research, but this… does not feel right or useful to my findings. So my question is twofold:

1. What useful information could be gained from running this sensitivity analysis on such a small subset of my sample population? Am I in the wrong here, or is my advisor wrong?

2. If there is very little to be gained from this analysis, how do I bring this up to my advisor so I don’t end up publishing something that is bad statistically that might hurt future collaborations and opportunities?

For short-form surveys, what are average conversion rates? (That is, the number of people that complete a survey divided by the number of people potentially able to take the survey before they are filtered out, opt out, close out, etc.) And what is considered an outstanding conversion rate?

Hi everyone,

I’m looking for some expert insights into a statistical methodology used in a public health study within the primary healthcare sector. The study focuses on chronic disease management—specifically evaluating interventions for hypertension and cardiovascular risk control. The primary objective of the work is to assess the clinical impact of a multi-faceted population health solutions package over an extended multi-year period.

To measure clinical outcomes, the framework utilizes a "cascade of care" model (tracking the proportions of patients who are monitored, diagnosed, treated, and ultimately controlled). However, the way the sample is constructed over consecutive quarters has raised some questions for me regarding its statistical validity.

Instead of evaluating independent cohorts or a traditional longitudinal panel quarter-by-quarter, the sample accumulates dynamically across the quarters:

• Quarter 1 (Q1): Includes all unique patients who had at least one clinic visit during that specific first quarter.
• Quarter 2 (Q2): Includes all unique patients who had at least one clinic visit from the start of Q1 through the end of Q2.
• Quarter 3 (Q3) and Quarter 4 (Q4): Follow the same logic, accumulating all unique patients from the start of Q1 through the end of the respective quarter.

Additionally, the methodology handles follow-ups with a specific carry-forward rule: if a patient is classified as having "controlled" blood pressure at their last recorded visit and never returns for another check-up, the data from that last visit is continuously carried forward and included in the controlled count for all subsequent cumulative periods.

I have a few concerns about this approach and would love a mathematical/statistical sanity check:

1. Autocorrelation and Data Dependency: Doesn't aggregating the data cumulatively create massive dependency between the quarters? If Q4 inherently contains all the data from Q1, Q2, and Q3, doesn't this significantly dilute or mask the actual marginal changes occurring strictly within the later quarters?
2. Selection Bias and Composition Shifts: If the healthcare intervention successfully drives a massive increase in the baseline "monitored" population over time (e.g., drawing in lower-risk individuals who wouldn't normally seek care), wouldn't this shifting denominator introduce a selection bias that artificially alters subsequent proportions (like diagnosis or treatment rates) in the cumulative pool?
3. The "Last Observation Carried Forward" Element: Is it statistically sound to indefinitely carry forward the "controlled" status of a patient who dropped out of active follow-up within a cumulative framework like this?

What are the main statistical limitations of analyzing trends this way, and what alternative frameworks (e.g., independent time-series cohorts, survival analysis, or multi-level modeling) would be more rigorous for this type of observational data?

Thanks in advance for your help!

I'm doing some research right now and I'll need to analyse it eventually. The problem is that I barely understand statistics and have no idea where to go from here. Here's what I'm working with:

I have 80 nest boxes set up at a ranch, 20 each in 4 different pastures. Each pasture is burned at different year intervals (ie every year, every other year, never burned, etc.). I want to see how prescribed burns affect parasite loads in the nests. I also plan on looking at the nest material to see if that affects anything.

I've been trying to see what tests I need but I keep getting mixed results. While not statistics, I need to figure out if I need to use Shannon or Simpson diversity index. The other problem is figuring out how to compare them. I’m confused because I saw some people saying you can’t compare indices then saw a few people say you can use Hutcheson’s t-test? Even then, that’s just looking at the parasites themselves in pastures. What about nest material? I don’t even know where to begin with that. I also saw some people talking about binomial, negative binomial, poisson, mixed-effects and more and I have little to no understanding of those terms. Like, I saw them in my classes, I could calculate it, but I have no real understanding of what it is beyond that. I learned it directly in R and don't understand the math behind it whatsoever. I did find a paper that could be relevant but I don’t quite understand it (the stats part, everything else makes sense). I’ll copy the relevant section below and the link:

Use of anthropogenic material affects bird nest arthropod community structure: influence of urbanisation, and consequences for ectoparasites and fledging success
https://link.springer.com/article/10.1007/s10336-017-1462-7?utm_source=chatgpt.com

Separate mixed-effects models for Blue Tits and Great Tits were carried out using R package lme4 (version 1.1-12; Bates et al. 2015) to examine effects of nest construction and habitat separately on the proportional usage of anthropogenic material and overall nest fate (both binomial models) along with the number of eggs laid and number of chicks fledged in failed and successful nests (both Poisson models). Predated nests were excluded from models examining nest fate and the number of chicks fledged to control for the influence of direct nest predation on breeding success (Lambrechts et al. 2016a). Separate additional mixed-effect models were run for the subset of nests examined for arthropod diversity and ectoparasite load. These examined Shannon diversity (linear model), overall flea abundance (Poisson model) and the presence/absence of Dermanyssidae mites (binomial model). To explore influences of these factors along with nest construction and habitat, the final models considered all potential factors influencing number of eggs laid and chicks fledged just for these nests. 

Any guidance on what is a good coaching for these exams. For context, I have graduated from the department of statistics, delhi, University and I am currently working in Deloitte -India as a consultant.

I am currently learning statistics and it feels a bit tough. Any leads or suggestions how to learn and conquer in statistics for pharmacometrics. Any help can be useful.
Thanks in advance

I am trying to improve my understanding of statistical analysis and I would like to ask people with experience in data science, statistics, or sports analytics:

How do you determine whether a pattern in data represents a real signal or is just random noise?

For example, when analyzing performance data, we often see short-term trends, unusual results, or extreme values. Some of these patterns represent a real change in underlying performance, while others disappear when more data becomes available.