r/AskStatistics

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.

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!

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 am working on my masters dissertation and I am having some trouble understanding the regression output from Stata.

I am trying to understand my multiple linear regression output from Stata. I understand that the p-value and confidence intervals in the Stata output are two-tailed so dividing them by 2 gives you the one-tailed value. However, I cannot seem to find any resource saying whether or not the t-value is one-tailed or two-tailed. please help!

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

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'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! :)

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 used a transformer model to generate normalized 768-dimensional float32 vectors from 3 million sentences. This rounds up to roughly 9 GB of data. To progress further in my analysis, I want to cluster the data to highlight different key points and trends.

Now I initially wanted to apply PCA to the dataset, as 768 dimensions is pretty much, but the transformer model generated embeddings with not extreme enough covariance.
Thus, PCA could change the semantics too much for an analysis to be accurate.

I went directly to clustering algorithms and initially tried to apply sklearn.cluster.HDBSCAN which would have needed way too much RAM---around 72 TBi if I remember correctly. Then I tried out sklearn.cluster.OPTICS, which "only" needed about 70 GB, but as I "only" have 32 GB of RAM I needed to use 50 GB swap space, which didn't go well as you can imagine.

Does it make sense to try out k-means clustering, should I go to 16-bit floats for the embeddings, or still apply PCA?

Should I try out something else in this context?

Note: As this question was flagged "off topic" on Stack Overflow, I'm gonna try here ;)

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 have a data series of daily streaming counts for a song. My spreadsheet has the date in column A and the number of streams in column B. There are 960 rows/records in the spreadsheet dating from 2024/01/01 to 2026/08/17.

We hired a marketing company to promote the song for 3 weeks. Their first posts on social media started on 2026/07/27. They ran until 2026/08/17. I am trying to assess whether our money was well invested or not. Their dashboard stats are not useful to us because they show the number of views and social media engagement (e.g., TikTok), whereas we are interested in the number of times our song gets streamed on a streaming platform (e.g., Spotify).

I know how to calculate means and standard deviations, and have done so for various timeframes (e.g., yearly, during the promotion, the 22 days prior to the promotion, etc) but I do not know how to:

1. determine if the 22-day marketing campaign had a statistically significant impact on our daily streaming numbers

2. determine if the impact on streams, if any, justifies the money invested, call it **CampaignCost**.

Can someone advise me how to go about this? We might assume somewhere between 0.001 and 0.003 USD of revenue per stream.

I know there is surely some well known statistical test to determine whether my daily streams show a statistically significant increase or decrease, but I cannot remember what this test is called or how to calculate it.

I might add that there are potential complications:

1. Streams have grown significantly year over year. Average daily streams in 2024 were 135k, in 2025 were 250k, and so far in 2026 are 259k.

2. Some unexplained events in the real world have prompted surges in streaming numbers. E.g., our streams surged upward for a month around December 2024 and remained elevated for months then gradually declined. Another unexplained surge arrived around Feb 5, 2026 and persisted for months but then started gradual decline.

3. The streams show a weekly cycle, lowest on Sundays, peaking on Thu or Fri.

Any help would be much appreciated.

I recently switched my retirement/financial planning model from assuming a fixed 8% annual return to using Monte Carlo simulations.

One thing I’m still trying to get comfortable with is how to interpret the results. The range of outcomes is also surprisingly wide. I understand why mathematically, but I’m curious how people use that range when making actual planning decisions.

For those of you who use Monte Carlo, what percentile do you typically pay attention to? Do you mostly look at the median outcome, 25th percentile, 10th percentile, or something else?

Hello, I'm a bioinformatician and I'm struggling with the current issue:

Given a time series y(t) that initially changes and eventually approaches a stable regime, how can I mathematically determine the earliest time t\* at which the rate of change dy/dt becomes negligibly small, using only the observed data and without defining an arbitrary threshold?

This is a collaboration I'm doing. My colleagues defined the plateau as the first time when a 101-point rolling mean of the relative increment (g' t+1 - g' t)/ g't falls below the arbitrarily chosen threshold of 0.0011. G' is the measure of material elastic-solid response btw. So the issues is that they used 2 arbitrary values because experimentally they know that a certain value of g' means that the gel is solid. But this doesn't hold for me. I tried using many statistical methods to define the threshold such as:

- exponential fitting

- change-point regression

- local slope analysis

But they all give me a plateau that is too early or too late

​

I’m a student from a small town in a backwards developing country , the education system is horrible. I’ve been recommended Saylor Academy, that's how I understand many statistics principles, and I’ve also started reading elementary statistics by Allan g. Bluman.

Still, I often feel far behind students from better educational systems. My goal is to become one of the greatest econometrician in my country.

For those who are in the field, what resources would you recommend? Textbooks, courses, math/stats, programming, or anything else you think is essential.

I’m willing to put in the years—I just want to make sure I’m learning the right things in the right order.

I have finished my college level statistics coursework and I can't even read the national statistics report I have to use AI to analyze it for me whenever I have to do research. I feel illiterate and slow.

I have a model that outputs a probability for each case, and I use a threshold on that probability to pick an action. The costs of a wrong action are asymmetric: one kind of mistake is much more expensive than the other, so where I put the threshold matters a lot.

My question is about trusting the probabilities themselves. Before I set a decision threshold, how do I check the predicted probabilities are actually calibrated, i.e. that a predicted 0.7 really corresponds to roughly 70% in reality?

I know reliability diagrams and proper scoring rules (Brier, log loss) are the usual tools, but I'm unsure how to read them in the context of an asymmetric-cost decision specifically. Does calibration matter uniformly across the probability range, or mainly near the threshold I care about? And if the probabilities are miscalibrated, is recalibrating (e.g. isotonic / Platt) before choosing the threshold the right order of operations, or should the cost asymmetry factor in differently?

I’m not a programmer and have used mainly menu driven packages in the past SPSS primarily although I have had some painful experience with SPSSx. Any R recommendation for intermediate stats background but not much syntax driven programming?

Thanks