I’m confused why your delay of again is longer than the delay of good. What’s your learning step setting?
I guess you use different learning step settings in different presets. I will move this stats to the old stats page, then it could filter the data by deck.
Yes, filtering the data by deck and an ability to exclude suspended cards would be a great improvement.
Thanks I figuted it out. I tested it on my all-encompassing deck.
Damn, just what I thought.
Stability at 1 is 3 seconds.
Stability at 3 ist 4,7 minutes.
Turns out I really do have a bad memory 
My current learning steps are 1s 3m. So are there any recommendations in my case 
I tested it on different decks and it returned the same thing so these stats apply to probably everything. So yes I agree 
Tried it on each individual deck and the data is pretty much the same.
Same here!
I ran this for my collection and got very similar statistics to what you posted in OP. However, when I leave the learning intervals blank, FSRS gives my first again interval at over 7 hours when my target retention is 90%. I’d have expected it to give something more like 15 seconds. Am I missing something?
That is inconvenient. But then again, this is just experimental and was just released today.
Now it works for separate decks, thank you!!
Here is the table for one deck.
Here is the table for another deck(I have never used the hard rating here).
But how can you interpret this data here For example, what does Review Timing Distribution mean
And could this eventually be used to allow the auto short-term scheduler to take custom learning steps into account
I still don’t understand what these numbers entail. And how I should deal with them.
You can read this wiki page: Quartile - Wikipedia
The first row shows when I press again in a new card, 25% of the next reviews are done in 1.2 min, 50% are done in 1.38 min, 75% are done in 2.22 min. The average retention of the next review is 83.48%. And the estimated stability is 0.8 min.
Amazing! This seems a really good step forwards for automated intraday intervals management.
Does it make sense for something based on these statistics to eventually replace the current optimizer for initial FSRS stabilities (w0-w3 iirc)?
The challenge is how to build a bridge between short-term memory and long-term memory.
Ohhhh. Now I starting to see things a bit more.
So this would change based on whatever learning steps I have, and based on that, the stability is calculated. If I remember correctly, Stability shows the time after which the retrievability has declined to 90%, so if I want a retention of 95%, I should aim for less time.
My learning steps for this is 1s 3m (auto intraday scheduler turned on).So what would be the recommendation here. Or does the short-term scheduler not yet have a link to custom learning steps
So If I understand this correctly:
1-My learning steps are 1s 3m. So I have a 1s again interval (0.02 min)
2-Having a 1s again interval does not necessarily mean I would see the card in 1s → delays (which is true from personal experience)
3-Having this delay influences my retention obviously
4-Based on the delays and retention achieved, stability is calculated.
5-In my case stability is 0.02 min, so I should be indeed be seeing the card in under 0.02 if I want a retention of anything >90%.
The stability is calculated from your review data. And changing the learning steps will let you generate different review data in the future. It may affect the stability.
However, in theory, the stability only depends on your first rating unless you change your rating habit.
I liked it before. T25% won’t be easy for non-math people.
Imagine that we split the review data into four equal part by the delay. Then we have four average retention for each part, and three thresholds of delay. It doesn’t need math.
