It's 2021! Time for a crash course in four terms that I often see mixed up when people talk about testing: sensitivity, specificity, positive predictive value, negative predictive value.

These terms help us talk about how accurate a test is, but from different viewpoints. 1/

Viewpoint 1 is about the status of the person taking the test. Are they infected, or not infected? How good is the test at identifying these people? That's sensitivity/specificity. 2/
A test that is very *sensitive* will be very good at accurately identifying people who are infected.

A test that is very *specific* will be very good at accurately ruling out infection in people who are not infected. 3/
Viewpoint 2 is about the result of the test itself. It says positive or negative (or detected or not detected). How much can those results be trusted? Did the positive or negative actually "predict" the situation correctly? 4/
A test that has a high *positive predictive value* means you can really trust a positive. Most of the positives that come out do really mean that person is infected. 5/
A test that has a high *negative predictive value* means you can really trust a negative. Most of the negatives that come out do really mean that person is not infected. 6/
Let's think about a population of 100 people. 10 are infected with SARS-CoV-2 (the coronavirus that causes Covid-19). All take a test.

Of the 10 people infected, 8 test + (true +), 2 test - (false -).
Of the 90 people uninfected, 89 test - (true -), 1 tests + (false +). 7/
The sensitivity of the test is true positives/(true positives + false negatives): 8/10. The denominator is, again, the status of the person: out of all the infected people, how many did the test catch? 80%. The test has a sensitivity of 80%. 8/
The specificity of the test is true negatives/(true negatives + false positives): 89/90. Out of all the uninfected people, the test correctly identified 98.9% of them. The test has a specificity of 98.9%. 9/
The denominator changes for ____ predictive values.

The test's positive predictive value is true positives/(true positives + false positives): 8/9, or 88.9%. It's the proportion of positives, out of all the positives, that were accurate. 10/
The test's negative predictive value is true negatives/(true negatives + false negatives): 89/91, or 97.8%. It's the proportion of negatives, out of all the negatives, that were accurate. 11/
So when you see that a test's specificity was 98.9%, for example, that doesn't actually, by itself, tell you the number of false positives that occurred.

It's also why specificity and negative predictive values for a test can be totally different numbers! 12/
That's it! Ta for now! 13/13

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OK I am going to be tackling this as surveillance/open source intel gathering exercise, because that is my background. I blew away 3 years of my life doing site acquisition/reconnaissance for a certain industry that shall remain unnamed and believe there is significant carryover.


This is NOT going to be zillow "here is how to google school districts and find walmart" we are not concerned with this malarkey, we are homeschooling and planting victory gardens and having gigantic happy families.

With that said, for my frog and frog-adjacent bros and sisters:

CHOICE SITES:

Zillow is obvious one, but there are many good sites like Billy Land, Classic Country Land, Landwatch, etc. and many of these specialize in owner financing (more on that later.) Do NOT treat these as authoritative sources - trust plat maps and parcel viewers.

TARGET IDENTIFICATION AND EVALUATION:

Okay, everyone knows how to google "raw land in x state" but there are other resources out there, including state Departments of Natural Resources, foreclosure auctions, etc. Finding the land you like is the easy part. Let's do a case study.

I'm going to target using an "off-grid but not" algorithm. This is a good piece in my book - middle of nowhere but still trekkable to civilization.

Note: visible power, power/fiber pedestal, utility corridor, nearby commercial enterprise(s), and utility pole shadows visible.

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I hate when I learn something new (to me) & stunning about the Jeff Epstein network (h/t MoodyKnowsNada.)

Where to begin?

So our new Secretary of State Anthony Blinken's stepfather, Samuel Pisar, was "longtime lawyer and confidant of...Robert Maxwell," Ghislaine Maxwell's Dad.


"Pisar was one of the last people to speak to Maxwell, by phone, probably an hour before the chairman of Mirror Group Newspapers fell off his luxury yacht the Lady Ghislaine on 5 November, 1991."
https://t.co/DAEgchNyTP


OK, so that's just a coincidence. Moving on, Anthony Blinken "attended the prestigious Dalton School in New York City"...wait, what? https://t.co/DnE6AvHmJg

Dalton School...Dalton School...rings a

Oh that's right.

The dad of the U.S. Attorney General under both George W. Bush & Donald Trump, William Barr, was headmaster of the Dalton School.

Donald Barr was also quite a


I'm not going to even mention that Blinken's stepdad Sam Pisar's name was in Epstein's "black book."

Lots of names in that book. I mean, for example, Cuomo, Trump, Clinton, Prince Andrew, Bill Cosby, Woody Allen - all in that book, and their reputations are spotless.
#தினம்_ஒரு_திருவாசகம்
தொல்லை இரும்பிறவிச் சூழும் தளை நீக்கி
அல்லல் அறுத்து ஆனந்தம் ஆக்கியதே – எல்லை
மருவா நெறியளிக்கும் வாதவூர் எங்கோன்
திருவாசகம் என்னும் தேன்

பொருள்:
1.எப்போது ஆரம்பித்தது என அறியப்படமுடியாத தொலை காலமாக (தொல்லை)

2. இருந்து வரும் (இரும்)


3.பிறவிப் பயணத்திலே ஆழ்த்துகின்ற (பிறவி சூழும்)

4.அறியாமையாகிய இடரை (தளை)

5.அகற்றி (நீக்கி),

6.அதன் விளைவால் சுகதுக்கமெனும் துயரங்கள் விலக (அல்லல் அறுத்து),

7.முழுநிறைவாய்த் தன்னுளே இறைவனை உணர்த்துவதே (ஆனந்த மாக்கியதே),

8.பிறந்து இறக்கும் காலவெளிகளில் (எல்லை)

9.பிணைக்காமல் (மருவா)

10.காக்கும் மெய்யறிவினைத் தருகின்ற (நெறியளிக்கும்),

11.என் தலைவனான மாணிக்க வாசகரின் (வாதவூரெங்கோன்)

12.திருவாசகம் எனும் தேன் (திருவா சகமென்னுந் தேன்)

முதல்வரி: பிறவி என்பது முன்வினை விதையால் முளைப்பதோர் பெருமரம். அந்த ‘முன்வினை’ எங்கு ஆரம்பித்தது எனச் சொல்ல இயலாது. ஆனால் ‘அறியாமை’ ஒன்றே ஆசைக்கும்,, அச்சத்துக்கும் காரணம் என்பதால், அவையே வினைகளை விளைவிப்பன என்பதால், தொடர்ந்து வரும் பிறவிகளுக்கு, ‘அறியாமையே’ காரணம்

அறியாமைக்கு ஆரம்பம் கிடையாது. நமக்கு ஒரு பொருளைப் பற்றிய அறிவு எப்போதிருந்து இல்லை? அதைச் சொல்ல முடியாது. அதனாலேதான் முதலடியில், ஆரம்பமில்லாத அஞ்ஞானத்தை பிறவிகளுக்குக் காரணமாகச் சொல்லியது. ஆனால் அறியாமை, அறிவின் எழுச்சியால், அப்போதே முடிந்து விடும்.