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naraburns  nihil supernum  1yr ago (text post)   Edited 1yr ago   5995 thread views

Quality Contributions Report for January 2023

This is the Quality Contributions Roundup. It showcases interesting and well-written comments and posts from the period covered. If you want to get an idea of what this community is about or how we want you to participate, look no further (except the rules maybe--those might be important too).

As a reminder, you can nominate Quality Contributions by hitting the report button and selecting the "Actually A Quality Contribution!" option. Additionally, links to all of the roundups can be found in the wiki of /r/theThread which can be found here. For a list of other great community content, see here.

This month we have another special AAQC recognition for @drmanhattan16. This readthrough of Helen Joyce’s Trans: When Ideology Meets Reality garnered several AAQC nominations throughout the month:

Part 1 – The History of Transgenderism

Part 2 – The Causes and Rationalization of Transgenderism

Part 3 – How Transgenderism Harms Women And Children

Part 4 – How Transgenderism Took Over Institutions And How Some Women Are Fighting Back

Part 5 – Conclusion and Discussion

Now: on with the show!

Quality Contributions Outside the CW Thread

@gattsuru:

@Rov_Scam:

@OracleOutlook:

@popocatepetl:

@AmrikeeAkbar:

@urquan:

@Chrisprattalpharaptr:

@DaseindustriesLtd:

@erwgv3g34:

@ymeskhout:

@aiislove:

@faul_sname:

@throwaway20230125:

Contributions for the week of December 26, 2022

@FiveHourMarathon:

@dr_analog:

Contributions for the week of January 2, 2023

@FiveHourMarathon:

@Rov_Scam:

@JhanicManifold:

@screye:

@problem_redditor:

@veqq:

@daezor:

@LacklustreFriend:

Contributions for the week of January 9, 2023

@naraburns:

@huadpe:

@Stefferi:

@FCfromSSC:

@DaseindustriesLtd:

@Dean:

Contributions for the week of January 16, 2023

@Dean:

@ControlsFreak:

@Stefferi:

@DuplexFields:

@ymeskhout:

@strappingfrequent:

@doglatine:

Contributions for the week of January 23, 2023

@gattsuru:

@TracingWoodgrains:

@arjin_ferman:

@vorpa-glavo:

@Amadan:

Contributions for the week of January 30, 2023

@gattsuru:

@TracingWoodgrains:

@DaseindustriesLtd:

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Er. I think I may not have explained clearly enough what I was doing there.

My purpose in listing those 5 names was not "make the account more moving by providing names instead of inhuman numbers". My purpose was to determine whether it was likely that those names corresponded to (1) real people who were (2) from a plausible area to be on that transport and (3) not obviously still alive after WWII.

If those names didn't correspond to anyone I could find details about pre-1940, that would have been evidence against that list of 4.8 million names corresponding to 4.8 million people. Likewise if the names and birth dates were repeated dozens of times, or if the documents looked like forgeries, or if there was an obituary from a 1976 newspaper about one of the 5 people and another two had gone on to have children in the 1950s. Those are ways the world could have looked.

In fact I got the outcome I pretty much expected. Which rules out a whole bunch of the specific ways "those 4.8 million names do not belong to Jews who died in Nazi custody during WWII" could be true.

As a note: you should not just believe me. I could have cherry-picked my random numbers. You should instead choose your own random numbers, and then test whether those random numbers appear to you to be people who did not exist / duplicated records / people who have a suspicious obituary in 1976, by looking at the world with your own eyes, which is a thing you are allowed to do.

Are you an auditor? What you did with the names would be referred to as "vouching": taking a sample from your population and finding the source documents for those in the sample, to verify management's assertion that those transactions actually exist.

I am not. We call it "random sampling" or "spot checking" in the professional context I inhabit (my role is dev / analyst (/ product manager / customer support / designer / qa / etc... can you tell I work at a small company?))

I do, in fact, do some fraud detection as part of my nebulously defined job responsibilities, though. For that my favorite heuristic is actually

  1. Pick a metric. Any metric. The stupider the metric sounds, the better. If you're running a marketplace, "fraction of orders with a positive subsequent review from the customer" is a good metric, but "average time from order to shipping label printed" might actually be better by virtue of not particularly sounding like it points at anything valuable.

  2. Rank all users by that metric.

  3. Take the bottom and top 5 users (with a substantial amount of account activity) by that metric.

  4. Most of those 10 users are probably trying to defraud you.

Ah, very cool. Sampling is a big part of auditing, as is directionality: vouching, for example, goes from final answer back to source documents, while tracing works the other way, from source to final, in order to verify existence and completeness respectively.

General auditing is directed more toward finding error than fraud, but forensic stuff interests me quite a bit.

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