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?

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!