I'm in the early phases of story development for a horror screenplay and wanted to dig into some probability stats

In my story young men are turning up drowned in a UK river. No evidence of foul play but a couple of patterns are discovered. The second can't be written off as coincidence

1) The men are between 25-45

2) The men are all widowers.

If there are 12 corpses found over a 12 month period... How would I go about giving both these patterns attributed to the death a figure (percentage or probability - I don't know???) that would win an argument between the lay person who discovered these two things and the police investigator who has closed each case, treated them as individual instances of drowning by accident - based on the evidence and pathology reports etc

Does my question make sense?

Thanks in advance...

Which one has better job outlook and opportunity? Will there still be good outlook in 3-4 years?

I am currently enrolled in Applied Statistics and on track to graduate with a B.S. in 2029. I think if I switched to Actuarial Science, my credit transfers would allow me to graduate sooner. I prefer the material that I am learning in Applied Statistics, I have the patience for calculus and analysis and find those things more interesting than what I’d learn in actuarial science, however I am afraid by the time I graduate I will be late to the party for a lack of better terminology . Any thoughts on this?

this is for my thesis ^^

Hi everyone, I have current and historical plant reproduction data from 40 stations. At each station, total number of current reproductive structures (flowers or fruits) were counted in each of four quadrats, and five random individuals were collected for analysis of historical flowering (resulting in a count of historical flowering events that each individual participated in). I want to see if stations that have high incidence of historical reproduction are also actively flowering this year. I understand the specifics of my question (prediction, correlation, etc.) determine what test I can use, but first I need to find out what tests I can even run with this data. My issue is that 21 of the stations have neither current structures, nor historical flowering evidence - so my data is extremely zero inflated. I have the raw quadrat/individual data, but I figured it'd be best to run this on a station level (idk???) - so I summed the total number of current reproductive structures and then the total number/incidence of historical events (across all individuals) within each station, producing the contingency table below (columns are stations with/without current flowering/structures, rows are stations with/without historical flowering). Any direction on what tests I am able to use to answer the (intentionally) vague question I want to ask? Based on my limited knowledge of zero-inflated I've tried spearman's rank, kendall's tau, mann-whitney U, fisher's exact test, non-parametric permutation test for GLM, and zero-inflated poisson model, just in exploration... but don't know if any are actually valid for my data, or if there's another test I'm not aware of/considering.

>print(contingency_table)

Absent Present

Absent 21 3

Present 11 5

Forgive me, I am but a field ecologist. Thank you for at least reading this far!

Hello,

I am analyzing electoral vote share across +200 municipalities using ArcGIS Pro and a Queen contiguity weights matrix.

Both LM Lag and LM Error tests were significant (p < 0.001), but the Robust LM tests favored SAR (Robust LM Lag p = 0.0017; Robust LM Error p = 0.6694).

However, after estimating SAR, residual Moran's I remained statistically significant (I = 0.283, p < 0.001).

Does this indicate that SAR has not adequately addressed spatial autocorrelation, or is conventional Moran's I inappropriate for SAR residuals? Should I also consider a spatial error model (SEM), despite the Robust LM tests favoring SAR? Which post-estimation diagnostics would be most appropriate?

Thank you!

Looking for help with the default weights from these statistical comparisons. I've run through a few variations and still not sure the best approach. Anyone have any opinions on this laughter and crowd analytics study? Rankings section has the adjustable weights section in question,.

This is what was measured, I didn't assign anything personally. Any cast is attributed to an entire skit regardless of involvement, so everyone shares the reactions from a segment.

Laughter rate (per comedy minute)

Laughter seconds (per comedy minute)

Runs per minute

Runs per 100 words

Median run Δ

Applause share

Peak confidence (laughter head's median run peak)

Resume Δ (seconds from a laughter run's peak to the next word),

Cheering rate

Cheering share

*edited for clarity

Hi all,

Has anyone ever performed stratified permuted block randomization in R? If so, which package(s) would you recommend? This is for a parallel two-arm clinical trial. The investigator was adamant about not using REDCap.

How often do you read on new statistical methods and read the research methods of different people?

Hello, I’m doing a secondary analysis of a cross-sectional observational study and I’ve been thinking about my statistical approach for way too long. I’m feeling a bit stuck and my supervisor isn't much help, so I would be very thankful for some input :)

Here’s the situation: I have an independent variable with 4 very unequal groups (n = 24, 70, 456, 1576). I must keep all four groups (my supervisor insists). Also, these differences in sample size are expected as it simply reflects the actual distribution in the general population, but I know it can be a problem.

I have 13 dependent variables from two questionnaires measuring two correlated multidimensional constructs (one with 8 subscales scaled 0-100, one with 5 subscales scaled 4-20). All 13 subscales are moderately to highly correlated with each other. The data comes from the general population, so a few subscales have severe ceiling effects (40-70%), while others have floor effects (10-20%). Distributions are skewed, and homoscedasticity is probably violated. I also need to control for age (in years), sex (binary), and education (categorical), as the IV is selective on these variables and they also influence the DVs.

I should examine the influence of the IV on the two health outcomes. But my supervisor wants me to analyze the data at the subscale level (no sum scores allowed), which is why I have these 13 individual DVs. The overall approach is more exploratory.

So, I had planned to run 13 separate ANCOVAs (one for each scale) and only control for the alpha error within the post-hoc tests of each model (treating each scale as its own family).

My questions are:
1) Is ANCOVA the best choice here, or is multiple regression better? I know both are cases of the GLM, but ANCOVA has stricter assumptions, particularly homogeneity of regression slopes. My supervisor says I shouldn't test this assumption as it's not relevant, but every textbook says it's essential. If I test it, my large sample size will likely lead to some significant interaction terms purely due to statistical power, forcing me into a moderated regression regardless of practical relevance. Prior studies similar to mine used ANCOVAs, but they had far fewer subscales and they never even reported testing any assumptions.

2) Is OLS even defensible in this case? Given the likely violated assumptions of standard OLS. Should I use robust regression instead or go about this a completely different way?

3) How do I handle the multiple testing problem? As mentioned, I only planned to control for alpha within the post-hoc tests of each model. But the Type 1 error would still be inflated across the whole study (especially since all 13 subscales correlate with each other). Is this approach defensible for an exploratory analysis or do I need to apply a correction across all 13 models (or across the two constructs)?

Thanks so much in advance :)

hello there! i'm a liberal arts major preparing for a master in statistics, and would love cool book suggestions. i'm already in love with topics like applied mathematical stats in ML, bose-einstein stats, and even statistical mechanics for that matter. even works like keynes' treatise on probability would interest me a lot. my purpose is to explore statistics, both philosophically and historically, and understand it's applications as well.
will be looking forward to all the great recs, thank you!

I am writing a paper comparing mortalities in a population with a specified sub-population. Are there any pit-falls or mistakes to avoid? There are already significant data showing correlation between the sub-population and mortality.

Thank you

I'm working on my first little scientific paper and I am really struggling to come up with how to measure the statistical significance of my dataset. I haven't taken a statistics class in forever, and I know the answer is probably ridiculously simple, but Google has not been helpful and I refuse to ask AI for help. I collected data on nighttime snake activity, and I want to have a way to show that these snakes are consistently active at a particular time of night. My data just consists of a list of timestamps when snakes were found. At a glance, I can tell that the times are clustered around roughly the same time each night, but I don't know how to actually statistically prove that they are. I don't have any other populations or data to compare to, and I want to keep the data as continuous time and not have to sort my data into bins. Please let me know what analysis would be best to "prove" my hypothesis!

I'm thinking about how the general public interprets scientific claims published and shared through popular outlets. It's very common for people who haven't even read past the headline to dismiss the study by saying "that [whatever findings were reported] is just correlation." But biomed and treatment efficacy literature for instance is rarely just slapping a correlation matrix together and calling it a day. The analyses typically do try to differentiate between causation and correlation in their experimental designs, though of course that doesn't mean the evidence is persuasive, that the sample is representative, etc. There is an argument that this is kind of a faux-scientific way of attacking a study without actually engaging with its logic of argumentation.

TLDR: Is correlation ≠ causation overused or improperly used to reject potentially important scientific findings that one simply disagrees with?

After talking to some professors and others online, they’ve recommended Kaggle competitions and solidifying my python skills, but I was wondering if there were other ways to increase my chances of a stats internship such as other platforms and personal projects?

My understanding is that downloading the csv files in Kaggle is not representative of the real world since it’s often always ‘clean’ and ‘sorted’ which is why I was wondering about other alternatives.

Additionally, I was wondering if stats majors often have to resort to Data science and finance internships or have college students been able to find actual statistician internships?

EDIT: I live in Australia

Hello everybody,

I am relatively new to statistics as a whole but now need to calculate the a priori sample size for a non inferiority study with a predetermined ni-margin, alpha, power and the results of a pilot study given. The metrics are not normal distributed and therefore as far as I know most online calculators are not applicable (or because of the study design as is the case in g power). I consulted ai for an answer and was pointed to calculating the sample size via R but I have never done this before and don't want to calculate it solely based on the code of ai.

Is there any other option, a tutorial or any code I can work with which isn't done by ai ?

Thank you for your help.

Hello!

I'm working on my uni project and have used Spearman test in it. How can I create the visualisation for the data? Preferrably online and free. I tried to search some sources, but could find only those who calculate the results, but I need a visualisation

Hi everyone, I am building a mixed-effects model to investigate how several explanatory variables affect an outcome. Based on my exploratory data analysis, I suspect that certain combinations of variables have three-way interactions.

For example, suppose I have four explanatory variables: A, B, C, and D. I suspect there is a three-way interaction between A × B × C, but I haven't found clear evidence suggesting interactions involving D.

My question is: If I include the A × B × C interaction in my model, do I also need to include other possible three-way interactions, such as A × B × D, A × C × D, and B × C × D? Or is it reasonable to include only the interactions that I have reason to suspect based on exploratory analysis or prior knowledge?

I'd also appreciate advice on the statistical implications of selecting interaction terms based on exploratory analysis, as I'm concerned that including only interactions that appear significant or interesting in the data might constitute cherry-picking.

Thanks in advance for any suggestions or references!

By applied statistics, i meant things like machine learning modelling, statistical modelling, predictive modelling. Basically data science stuff and statistics used to solve business problems and model real world Phenomena using Python and R.

Are they related to each other? What are the similarities and differences?

If you hop on r/statistics and this subreddit, you will usually learn about many methods. Some are locked into certain fields like ecology and finance. Others will touch on methods that deal with missing data (ex. MICE) and small samples (ex. Bootstrapping). Others talk about graph theory, MCMC, NUTS, and other methods that people don’t usually learn in a typical statistics class.

Will there come a time where you need to read on additional methods or will everything taught in statistics class be enough for any venture in psych research?

I read a masters/phd research paper from my country and it didn’t use anything crazy. Just stuff like pearson’s moment correlation, multiple regressions, spearman rank, etc, hence the question

Hello, I’m planning on taking an introductory statistics class over the winter and I was wondering if this is a bad idea? I plan to put the work in although I don’t want anything under an A. And another thing is Khan academy’s 16 unit overview of Statistics and probability a good way to prep ahead of time?

I'm involved in research on a demolished cemetery in Baltimore. We have been transcribing death certificates to understand the burial population. The problem is that death certificates only started to be recorded 23 years after the cemetery was established. We have 30,000 records of burials after that point. I plotted the number of burials per quarter from the records we had transcribed and in minitab, I did a trend analysis fitting different regression models, and the data best fits an exponential decay in rates of burials. The question is, how can I estimate what the curve would look like for the first 23 years for which we have no data. Is there a way to use the shape of the curve of the best fit line I have, together with a known 0 for the quarter before burials began to estimate how many burials may have taken place in the first 23 years? I feel it should be possible but I've approached the limit of my math/stats knowledge. Any advice would be greatly appreciated.

as the title imply can we really implement a hybrid equation like MCMC sampling with adaptive walk towards more accurate estimation ,I think what I'm describing in my mind is like something like hybrid heuristic

How do we decide the number of repetitions for monte carlo?

As I am approaching my first unit test, I was thinking of ways to study. I went to look for videos online, but many of them are sub 20 minutes, ai, low quality, and overall dont actually go over coursework, instead simply giving you a brief overview of the topics. So I have come to ask the experts, who did you watch as fledgling statisticians? I am starving for actually quality content, preferably 20 minutes or more, and actually goes over topics, applications, and examples. tysm!

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