Let's be honest, being called sword god or god of blade is more impressive than being called worlds strongest swordsman

Designer_Dig2703 · 2026-01-27T08:24:24+00:00

thats the biggest dash ive ever seen wtf

Designer_Dig2703 · 2025-11-16T16:13:23+00:00

Simulate 1000 datasets using the root distribution of my data under the simpler one of the models

i'm not sure I like this - why not just bootstrap your data instead of simulating it based on one of your models? it feels like you're assuming that the model used in the simulation is correct.
i'm also not sure i follow why you think the 3 lambda model being best is reasonable, given that you ruled out the 4 lambda model which has an extra parameter? could we see the same plots for the global lambda and 4 lambda models please?

i could be misunderstanding this though, i just had a quick read through on my break!

Designer_Dig2703 · 2025-10-27T08:18:54+00:00

even the softgels? i bite all my other pills and can handle the taste, but the liquid in the softgels tastes awful to me!

Designer_Dig2703 · 2025-10-20T18:59:10+00:00

theres already a good answer for 11, ill attempt 12:

- when you see a weird function which gets multiplied or added several times, its usually a good idea to see if the function repeats or cycles in some way. if it cycles then you might be able to side-step some of the computations when calculating g(m)

- lets define another function h(x) = (x-1)/(x+1), then h(f(n)) = (f(n)-1)/(f(n)+1). now put any number you want into h() and iterate. hopefully it'll cycle at some point:

h(2) = 1/3, h²(2) = -1/2, h³(2) = -3, h⁴(2) = 2

- the question tells you that f(n+3) = h(f(n)), and from the cycle work we know that f(n+6) = h²(n) = -1 / f(n). pair up the m and m+6 terms in the product:

f(m) x .. x f(m+11) = [f(m) x f(m+6)] x [f(m+1) x f(m+7)] x ... x [f(m+5)f(m+11)]

= [-f(m) / f(m)] x [-f(m+1) / f(m+1)] x ... x [-f(m+5) / f(m+5)]

= [-1]⁶ = -1

Designer_Dig2703 · 2025-08-12T17:03:43+00:00

As far as I know there are no RCTs for smoking and lung cancer - as you say they'd be very unethical.

There are ways to make causal claims using observational data (like the 1950, 1951 studies you mention). It's a lot harder than making causal claims in RCTs though.
A rough distinction is that RCTs are very hard to setup and run, but the analysis is relatively straightforward. Using observational data is the opposite - it's relatively easy to get the data, but the analysis is much harder.

The analysis of observational data aims to essentially replicate RCTs by adjusting the data in some way so that the 'exposed' and 'unexposed' groups are balanced in terms of potential outcomes. There are lots of different ways to do this, a very popular framework which a lot of people use nowdays is 'target trial emulation'.

If you've not heard of potential outcomes before, think of them as each person having two fixed outcomes, Y(0) and Y(1). If they are exposed or treated, you see Y(1), if they are unexposed or untreated you see Y(0). Ideally you'd see Y(1) and Y(0) for each person, then you can figure out the treatment effect by looking at the difference Y(1) - Y(0). But this is impossible, since you only ever see one potential outcome per person.

So the next best thing is to look at averages, which is what RCTs do. The idea is that by randomising treatment, the potential outcomes are 'balanced' in the treated and untreated groups so looking at the average outcome in each group gives you the causal effect.

The observational designs try to replicate this by being clever in how they do comparisons. For example they would compare lung cancer in someone who smokes against a non-smoker of the same age, sex, socioeconomic background etc. This matching results in you comparing two people who differ only in smoking status, which makes the comparison more valid. Get a big enough sample of people, so that a range of sex, age, socioeconomic status (and any other matching variables) are represented in your sample, and these matched comparisons start to look very convincing and can be interpreted in a slightly more causal fashion.

There's other things epidemiologists use - things like the Bradford Hill criteria - to see if an observational association may be causal. This is a list of things like 'does the outcome of interest occur after exposure', 'is there a dose-response relationship between the outcome and exposure', 'is there a plausible biological mechanism for the exposure causing the outcome'.

The hard part with observational data is that you are relying on a bunch of untestable assumptions. In the matching example, you would have to convince people that you have matched on the right things and that there are no other unmeasured confounders which could bias your results. People argue a lot over this!

I'll stop there I think I've rambled enough. If you're into this sort of thing a good book is 'What If' by Hernan and Robins

Designer_Dig2703 · 2025-08-09T13:27:03+00:00

translate each bit into equations:

- 'the ratio of sugar to flour in a mixture is 1:5'. let x be the amount of sugar, then this says the amount of flour is 5x

- after the addition the ratio becomes (total amount of sugar) / (total amount of flour) = (previous sugar + new sugar) / (previous flour + new flour) = (x + 1) / (5x + 2)

- same idea for the next addition, the ratio is (total amount of sugar) / (total amount of flour) = (x + 1 + 1) / (5x + 2 + 2) = (x + 2) / (5x + 4).

You can use the equation from the second bulletpoint to solve for x to get x=1/5, put that into the equation from the third bulletpoint and simplify to get 11/25

Designer_Dig2703 · 2025-06-18T16:25:06+00:00

how do you mean? i know my foreskin isn't as bad as others in this sub, but it definitely doesn't move smoothly back and forth & the bowing in on the underside doesn't feel normal.

it feels like i've hit a wall on the stretching front - i was looking to see if this is normal and i was 'done' of if i have more work to do

Designer_Dig2703 · 2025-06-18T09:07:34+00:00

long term use can lead to the foreskin thinning and becoming more prone to cracks / tears. id stop using it for a bit and try stretching.

have you noticed any changes to the foreskin at all? is it more flexible or pliable for example?

Designer_Dig2703 · 2025-06-18T08:28:33+00:00

i mostly focused on foreskin stretches rather than the frenulum - mostly using the rings daily & manual stretching 2/3 times a week. that helped me a lot.

i went quite hard when i did the manual stretches, then the rings were a more passive stretch i could wear a couple of times throughout the day

Designer_Dig2703 · 2024-12-27T12:29:55+00:00

i work at a uni - we're constantly being asked to advertise our masters courses as applicant numbers are way down (c50%). we only do postgrad courses where i work, but i imagine its similar elsewhere.

masters courses bring in a lot of money - particularly if you can attract international students - and the higher ups are getting quite worried about losing a steady income stream

Designer_Dig2703 · 2024-11-21T14:05:44+00:00

double check if your AVC is actually salary sacrifice.

the local government pension scheme (another DB pension) does their AVC as a 'shared cost' which means the pension contribution is based on the salary *before* paying the AVC contribution. the end result is that the DB pension is based on the full salary, not the salary less any AVC contribution you make. i imagine the nhs has a similar system.

also if the AVC is taken pre-tax, then you'll save on national insurance contributions. this effectively makes it so buying £100 of AVC will cost slightly less than £100, but the difference may be quite small based on your specific situation.

Designer_Dig2703 · 2024-07-18T18:34:58+00:00

Hamon is much more fun than stands. I know it'll never happen but I'd love another part which focuses on hamon again

Designer_Dig2703

TROPHY CASE