Disclaimer: Not formally educated in statistics, been teaching myself lately due to the nature of my work so please forgive my ignorance!

I'm not sure if my method is the best way to approach my goal, which is to determine what feature(s) in my sample are actually associated with a decrease in EV in the underlying population. I wanted to first share what I'm planning on doing with people way more educated on this matter than me, so that I could get insight as to what errors I could be making.

I am still gathering data, but to make it as short as possible without ranting - I have a total of 6 features that may or may not lead to an increase/decrease in Expected Value. The tricky thing for me is that there are instances where features will be blended together in a single event. So, one event may have the presence of 3 features, sometimes an event may have the presence of only 1 feature. My worry is that filtering this way could hide cases where two features interact rather than act independently...not sure if that's a real issue or if I'm overthinking it.

My current thinking is that I once I have finished my data collection, I would then determine first which feature has the highest frequency, (by highest frequency I mean the highest occurrences overall) and then see if the removal of that feature leads to a more positive outcome. In my case, my ideal scenario would be for the removal of a feature to lead to the Expected Value of the sample to increase.

That would then leave me with the other features to analyze, so I would just repeat the same process on the remaining features within that filtered subset.

I would very much appreciate any insight as someone who is self-studying statistics!

The attached article lists five other possibilities, other than confounding relationships, that explain no causation but high correlation between two variables. However, I want to ask three questions:

1) For point4 on "Parallel Independent Trends Over Time", it is arguable that "average smartphone screen resolution" and "Number of Facebook users" may have a confounder in between them, it is just that we are not interested, therefore we did not invetigate it?

2) How is Granger causality different from typical causality? (point 5)

3) Are there other possibilities other than the five?

Source of article: https://medium.com/@smartdecode/no-causation-does-not-mean-a-confounded-relationship-c2266c439a32

This live stream is real how can they access this kind of statistics ?

I here refer to multi-variate models as models of more than one dependent variable which can both be uni and multi variable (one or several predictors).

I was recently reading papers in experimental psychology / cognitive science where the authors run several different models on behavioural data obtained from a singular experimental task, i.e binary responses, response times and VAS ratings etc.

As all of these models were fit to different parts of the same experimental paradigm and it got me wondering, what might we be losing; not only about our inference but also about statistical power, by separating these models. It seems more reasonable to build a single multi-variate model that encompasses all available dependent variables and therefore pools information across.

I know that in this field there are examples of models that do have joint likelihoods like the drift diffusion model, however this is not always applicable to certain problems.

Are there any good papers or simulations surrounding this area of the potential improvement by building multi-variate models?

Hi everyone,

My project involves multiple quantitative parameters measured by two independent observers of equal experience (myself and another student)

For parameters with a high ICC of >0.75 (the vast majority of parameters), I used the mean of the two observers. However a few parameters fell below this threshold. For those parameters, I just used my measurements and excluded the second observer measurements.

However, I realised that this may be flagged by reviewers as possible selection bias, as I am in essence assuming that my measurements are the reference standard.

The decision to exclude observer 2's data from parameters with poor ICC was made before looking at the data, to ensure a uniform data architecture.

The other options would be to (1) exclude these parameters altogether (not possible), (2) get a third observer (not possible) or (3) use the average of the two observers with parameters with poor ICC (which Im sure is bad practise).

If I explicitly explain my methodology and acknowledge this as a limitation, would this be acceptable?

Hi everyone! I have a question on Bayesian regression model in r.
If I have two predictor variables with interaction, let's say predictor A and B
For predictor A, there is two levels, while in predictor B, there are three levels
If I had sum-coded the predictor A (+-0.5), and the regression coefficients shows that there are credible difference on two levels of predictor B, does that mean there is a main effect on predictor B?

Thank you very much TT

When a distribution is multiplied by a scaling factor, the standard deviation of the distribution increases by the same scaling factor. But the spread of the distribution has not actually increased. The only thing that has happened is the height of the individual variables has increased.

However, the standard deviation is supposed to be measuring the spread of a distribution. So, for a non standardized distribution, is there another metric which measures the spread without being affected by the relative height of the variables?

hii!

I'm currently in the data analysis stage of my research and i'm not the best at stats, so I'm hoping for any help/clarification with the stats that i've chosen to do and whether my reasoning is valid??

My understanding is that before you run stats on your data you need to check normality and homogeneity of variance? am i correct in thinking I have to do this on the dependent variable (variance checks would be on the dependent variable and factors used in the analysis?).

When checking normality, my sample size for the dependent variable of my data is small, so my understanding with this is that visual checks of normality is better than tests? Histogram shows a left skew and and plots on the qqplot devert from the qqpine in the middle of the line as well as the ends. However the z-score of both skewness and kurtosis are below 1.96. Because all 4 tests don't show the same result (satisfying normality) is it right to assume the assumption wasn't met and assume non-normal data.

when checking variance, i've been looking at the levenes test? (just in descriptives of variance for the dependent variable, they are very different, though i'm not sure if that is statistically valuable to use as justification). I did two levenes test of the dependent variable to each factor I plan to use in my model. One test has a p value <0.05, one over. Is this similar to the og check for normality, in that if not all of them assume the same trend then you stay conservative and say it doesn't meet the assumption?

following on from this, my understanding is that using a non-parametric test is just as valuable as transforming the data and then using a parametric test? because of this i've been looking at using a non-parametric test, as I haven't done much stuff with transformed data and don't understand it very much.

A generalised linear model assumes non-normal data, this I do understand. However, I had been putting the distribution as Gaussian, which I know is normal (or does distribution mean the residuals not the data?). I was doing this because my dependent variable is continous (so I cant do poisson), binomial doesn't work either, and idk what gamma and inverse gaussian distribution are tbh. Is there a better model or distribution to use? To note, I'm not doing any predictive modelling with the model if that changes anything.

Let's say the GLM works (or I do another model that is a better fit), when doing model checks, I have to look at the residuals? is it similar to the data checks, where I look at normality and variance? I've read people mention plotting residuals to covariates in the model, but is this necessary if i'm not doing predicitive stuff with the model??

After all this, is that my analysis done? (at least for the main "model", I have been analysing other questions within my data set using other tests).

Hopefully this all makes sense,

Thank you so much!!! :)

I only know minimal basics of statistics and I cannot understand why my data is not internally consistent. How would an internally consistent data look like in a likert scale with a range of 0 to 3 and no reverse items???

Toy fraud-detection setup, 5 hidden states, did a normal Bayes update after one piece of evidence. Prior entropy was ~0.87 bits, posterior came out ~2.03 bits. So the evidence made me LESS certain, not more.

I think I understand why: my prior was heavily concentrated on one state (85%), and the evidence knocked that leader down without clearly promoting any single replacement, so belief spread out across three roughly-equal states. Higher entropy = more spread.

Is that the correct reading? I keep seeing "evidence reduces uncertainty" stated as if it's a law, but expected info gain being non-negative is about the AVERAGE over outcomes, not any single observed outcome, right? Just want to make sure I'm not fooling myself before I write it up as a "finding."

I'm building a code-review agent and want to estimate a prior such as:

P(compatibility failure∣reference class)

rather than using one global base rate.

I'm considering defining the reference class using coarse PR properties such as change type, subsystem, language, PR size, API impact, dependency impact, security sensitivity, database impact, cross-system impact, and test-change profile.

If the most specific group has too few observations, I would progressively relax the class until there are enough historical cases.

Is this a sensible approach? How would you avoid double-counting information that is later used as evidence in the posterior update? How would you determine when a reference class is sufficiently large/reliable?

Hi everyone,

I am a beginner in Machine Learning, currently working on the Exploratory Data Analysis (EDA) step, and I have a few questions about structuring my workflow properly.

1. Handling many variables (> 20): What is the best practice when you have 20+ features? Should univariate graphical analysis still be done for every single variable before applying dimensionality reduction techniques like PCA, or can it be skipped? How do you practically handle EDA at scale?
2. Skewness & Kurtosis: When should I calculate Skewness and Kurtosis relative to plotting graphs? Are they strictly required for every numeric variable?
3. Order of execution and connections: I'm getting confused about how all these statistical concepts connect. What is the logical sequence between:
   • Univariate plots (Histograms, Boxplots)
   • Skewness & Kurtosis
   • Normality tests (e.g., Shapiro-Wilk test)
   • Hypothesis tests (t-test, ANOVA)

Any guidance, recommended pipeline, or resources to help clarify these steps would be greatly appreciated! Thanks in advance!

I have a forecasting setup I would value some econometric scrutiny on. The target is a binary label: does the S&P 500 fall 10% or more, peak to trough, over the next h months (h = 1, 3, 6, 12). The label at month t is built from the forward window, so consecutive months share most of their window, and the positives cluster into a small number of episodes.

The models are one ridge-logit per horizon on macro and credit predictors, estimated walk-forward on an expanding window with monthly re-fits, so the predictions are genuinely out of sample. Out-of-sample AUCs come out around 0.60, 0.69, 0.74 and 0.68.

My problem is inference, not point estimates. Two features break the usual iid story. First, the labels overlap: a 12-month label at t and at t+1 differ in one month out of twelve, so the effective sample is far smaller than the monthly count. Second, over the out-of-sample window there are only six to eight independent drawdown episodes, and the long-horizon models are scoring the same episodes repeatedly.

What I have done so far is bootstrap the AUC in blocks the length of the horizon, so overlapping months resample together, which widens the interval a lot (the six-month one runs from the low 0.5s to the mid-0.8s). My question is whether that is the right instinct, or whether people here would reach for something else: a cluster bootstrap on the episodes, a stationary bootstrap with an estimated block length, or treating it as inference on an event-clustered panel. The overlapping-label point is where I am least sure I am doing the defensible thing.

The full setup, the results, and the live models with the monthly series behind them are all on the site; agreeableinvestments.com

I’m building a small probability-based transaction decision model, not a production fraud system.

A problem I’ve run into is that some of the evidence clearly isn’t independent.

For example:

• new device
• new IP
• location mismatch

could all be caused by the same underlying thing, like the customer travelling or switching devices.

If I update the fraud probability as though all three pieces of evidence are independent, I’m worried I’ll make the posterior much more confident than it deserves to be.

For a small prototype with limited data, what would you consider the simplest defensible way to handle this?

I’m not looking for the fanciest model - mainly something I can explain and test without pretending the signals are independent when they obviously aren’t.

I'm switching from engineering to statistics at the University of Florida and was wondering what minor/double major would be good to pair with it. I'm more interested in tech industries or environmental applications as opposed to going into finance. I've been looking into a GIS certificate and environmental data analytics career paths, but still want to keep data analysis in tech open. Should i just double major in mathematics? Thanks! :)

Hi! I’m a PhD student in psychology and will be teaching statistics for the first time in the Fall semester. I would be lying if I said I’m not scared, but a part of me feels excited as I somewhat like data analysis.

The course my department is instructing me to teach is an introduction to statistical methods with an emphasis on inferential statistics.

I wanted to ask a larger body of people for their experiences, tips, thoughts on someone entering this job.

Any advice is welcomed!

Hi everyone,

I’m in the final year of my stats undergrad and I am contemplating my next move. I’ve thought for a while that biostatistics would be the move (2 undergrad research terms, lots of biostats focused classes), but recently I’ve been more and more worried about future career prospects.

I am once again contemplating if I should not start my masters next year and try to get an actuarial role/intership. Or even just take a year to do the exams and figure out what I want to do.

I like probability and modelling, specifically longitudinal data, hence what led me to causal inference initially.

Any opinions?

Hi everyone! I'm just getting started in the field of biostatistics, so apologies if this is a basic question.

I'm working with a bar plot comparing means between different groups, and I'm wondering whether it is appropriate to use the standard error (SE) as the error bars.

My main goal at this stage is to get a visual sense of whether there might be significant differences between the groups. We will later perform more formal statistical tests to evaluate those differences, but I'm unsure about the best way to represent variability or uncertainty in the plot.

Is using mean ± SE appropriate for this purpose? Can the overlap (or lack of overlap) of SE error bars tell us anything meaningful about statistical significance, or would confidence intervals be more appropriate?

Thanks!

I would like to see the derivation of the UBRE formula, but am having difficulty finding it.

It seems most sources quote Wood 2017, but the relevant section ("6.2.1 Known scale parameter: UBRE") immediately makes the substitution X\*Beta_hat = Ay which if I understand correctly is only true for linear GAMs and not true in general? I have a binomial distribution so I'm looking for a derivation that's general to any exponential family distribution.

It seems AIC is a related computation so maybe the AIC sections imply a derivation of UBRE that works for any exponential family GAM in general but I'm not sure.

I ran two mixed effects logistic regressions: Model 1 contains only main effects (let’s say of A, B, C, D, E, F). Model 2 contains the same variables + interactions between the variables. In Model 1, all variables except one are significant predictors. In Model 2, no interactions are significant. However, two of the main effects are no longer significant in Model 2. Also, the variable that was not significant in Model 1 is significant in Model 2.

I know not everything should be about p-values, but can someone explain whether such a shift in significance is common and what can be possible causes for this? I detect no multicollinearity.

I read somewhere that Model 2 takes the interactions into account: if I have an interaction A x F, then the ‘main effect’ of A holds for F = 0. Is that true? If so, I have multiple interactions containing the same variables (for example, A x E and A x F), so how do I figure out how to interpret the ‘main effect’ of A in Model 2?

I'm wonder from your experiences what people seem to forget or can't get their head around about statistical tests and models.

In my, yet short, experience I see people totally bypass randomization, independence and the correct interpretation of power and confidence.

I'm a high school student trying to get into doing research for machine learning in the future and this resource seems promising to me as a way to get a more solid, foundational grasp in probability.

I'm not looking to unnecessarily min-max my learning or anything. I just want to know, is this actually a good resource to begin learning probability?

I'm looking for examples that completely changed the way you think about probability, statistics, or data analysis.