Trust in the numbers is extremely important and we should all be calling out that we need to trust the numbers and the methodology. It should also be transparent.
However, it's extremely common in forecasting to revise the forecast once actuals come in. In the case of the BLS, it's the documented approach for a very long time.
Every month the numbers are adjusted and annually. All of the notes as to why, the method, etc are in the actual reports*.
*I don't recommend reading them or the footnotes unless you have insomnia. :)
** Also, if the source data is inaccurate, corrupted, etc; if the models are non-transparently adjusted, that would be horrible and cause for alarm. At the moment, we don't know if that is the case. Yet.
(reposting a version of my comment somewhere below)
The BLS (USA) does adjust the numbers every month (for two months after the initial release) and annually. Regardless if the numbers go up or down, this is fairly common with statistics and forecasting in general. When actuals come in, the forecast is adjusted closer to reality.
Anecdotally: It gets lost in the mix of headlines when those adjustments show that the initial projections were on trend, or "close enough the talking heads don't care enough". However, it gets "interesting" when it's off-trend; or confirms prior notable good/bad news. In this case, it confirms* what was suspected, mostly confirms what was reported. As actuals came in, the reality was worse than projected.
*"Confirms" use case here: job growth is poop right now.
Regardless of which administration is in place, the BLS (USA) does adjust the numbers every month (for two months after the initial release) and annually. This is fairly common with statistics and forecasting in general.
Regardless if the numbers go up or down, regardless of the administration.
So, yes...the last administration did the same thing, and the administration before that, etc.
I was just talking to younger coworkers about this recently. Mid-90s to early 2000s: FORTRAN, COBOL, C, and C++ classes all had handwritten code parts for homework, handouts, exams, etc. This wasn't just pseudocode, you had to have full syntax, variable declarations, correct spelling of functions, etc. You frequently had to show code optimization, debugging, etc even on paper. Wild times!!
* All of those classes also had lab time (some dedicated, similar to a chemistry class), info on how to get the IDE if you had $ access to a computer at home, and alternatives as well.
Personally, I see more value in pseudo code (written or typed) and sketch type diagrams (analog or digital) than handwriting code. However, it was WILD and amazing to watch the gray-hairs of those days debug your code on paper!
Similar for me. MacBook Air M1 (8 cpu / 8 gpu; 16 GB RAM)...running in or out of clamshell with a 5k monitor, I rarely notice issues. Typically, if I'm working very inefficiently (obnoxious amount of tabs with Safari and Chrome; mostly web apps, Slack, Zoom, Postman, and vscode), I'll notice a minor lag during a video call while screen sharing...even then, it still keeps up.
(Old Pentium Pro, PII, multi chip desktop days) -- When I did a different type of work, I would be in love with these new chips. I just don't throw as much at my computer anymore outside of things being RAM heavy.
The M1 (with 16 GB ram) is really an amazing chip. I'm with you, outside of a repair/replacement? I'm happy to wait for 120hz refresh, faster wifi, and longer battery life.
This is an odd hot-take for an active globally invasive and life altering virus.
That person has assessed their risk level and determined that their community's disregard for their personal risk, negatively impacts their personal model. Therefore they have to use a different device to mitigate their risk.
Seems pretty logical, matches with known science, with a minor exception that the half face respirator may not offer greater protection vs a well fitting* N95/KN95
Just to hop on this, the standard is counter intuitive for rain probability. I'm annoyed, but also fascinated by how much it "makes sense" when I have read about it, but it also "makes no sense" when I just want to know: should I get my jacket? :)
Loose definition (with errors and assumptions, but helps some without getting into actual probability math):
If it says "30%" - many of us have heard "30% chance of showers". It is NOT "30% chance of showers"; it actually is "100% percent chance of rain, over 30% of the area, within a given time, of a particular amount of rain."
Which is STILL (to me), difficult for the average person to comprehend.
PRECIPITATION PROBABILITY
The probability of precipitation forecast is one of the most least understood elements of the weather forecast. The probability of precipitation has the following features:
..... The likelihood of occurrence of precipitation is stated as a percentage
..... A measurable amount is defined as 0.01" (one hundredth of an inch) or more
(usually produces enough runoff for puddles to form)
..... The measurement is of liquid precipitation or the water equivalent of frozen
precipitation
..... The probability is for a specified time period (i.e., today, this afternoon, tonight,
Thursday)
..... The probability forecast is for any given point in the forecast area
To summarize, the probability of precipitation is simply a statistical probability of 0.01" inch of more of precipitation at a given area in the given forecast area in the time period specified. Using a 40% probability of rain as an example, it does not mean (1) that 40% of the area will be covered by precipitation at given time in the given forecast area or (2) that you will be seeing precipitation 40% of the time in the given forecast area for the given forecast time period.
Let's look at an example of what the probability does mean. If a forecast for a given county says that there is a 40% chance of rain this afternoon, then there is a 40% chance of rain at any point in the county from noon to 6 p.m. local time.
This point probability of precipitation is predetermined and arrived at by the forecaster by multiplying two factors:
Forecaster certainty that precipitation will form or move into the area X
Areal coverage of precipitation that is expected
(and then moving the decimal point two places to the left)
Using this, here are two examples giving the same statistical result:
(1) If the forecaster was 80% certain that rain would develop but only expected to cover 50% of the forecast area, then the forecast would read "a 40% chance of rain" for any given location. (2) If the forecaster expected a widespread area of precipitation with 100% coverage to approach, but he/she was only 40% certain that it would reach the forecast area, this would, as well, result in a "40% chance of rain" at any given location in the forecast area.