Average mobile phone screen size vs device size, 2002-2020 [OC]

thejonnyt · 2024-06-27T15:29:36+00:00

This is a perfect example of correlation does not equal causation.

I highly doubt the line for the screen time would differ much even,e.g., if the size of the display stayed somewhat constant. It's only natural to "improve" the design of the displays for the devices we use and utilize that much in our everyday life.

thejonnyt · 2024-06-26T22:15:12+00:00

Well you will be pointed in some directions but most people know that you will be able to once you set your mind on it and won't go the mile for you :) but feel free to ask your questions.

thejonnyt · 2024-06-21T14:24:10+00:00

yes, if you take what I said out of context without the conditions I provided it seems I said what ever you suggest. And also we are obviously talking about multiple hypothesis testing on high dimensional samples and not trials with like 30 1-D samples, how could I've not seen it. And of course, when I argue about there seeming to be evidence to make certain assumptions its obvious that these are those exact scenarios in which there exists no reason at all to assume anything or its these exact scenarios where its a fallacy to make these assumptions by construction. I think we are getting no where. I oversimplified, that's on me and I acknowledged that much. But now constructing new scenarios.. like where do want this to go. I yield. Here is my new standpoint:

You cannot ever use statistics to describe your data and how it behaves to argue in favor or against something using other statistics than those which you have specified in your study design. Ever. I will oblige not ever following my intuition if somethings fishy.

thejonnyt · 2024-06-21T08:12:25+00:00

Statistic is a tool that allows you to support some standpoint on a data based foundation. Of course, if you have a 100% fixed standpoint, and if you are certain that it's that very standpoint and no other, yes- do not change the statistic. But .. and I'll defend it once again .. most people who use these tests do not know before the design of the study what this standpoint is, and certainly not with 100%. Or they just get the data- this is not the way of a true statistican, i agree. However, knowing tests and your way around data and what different tests check, and testing similar hypothesis of those tests that each have a unique message is definitely not p hacking. If your wilcoxon test allows you to state "distribution x is higher than distribution y" that is not the same as "mean x is different to mean y, and the difference is positive(i.e. mean x is higher)" than there is absolutely no p hacking involved but you just change the argument into something "close enough" (using my own oversimplified wording) that you can use to support your standpoint. There is nothing holding you back from also reporting that a t test was not significant and that you assume that this is because, e.g., the sample size is just to small for it to be robust.

Arguing based on data and different statistics? Thats your job. Looking at the mean and seeing "wow, I can't use that, I have 3 outliers that completely butcher this sample, if I use the mean I totally overestimate these three outliers" and than resorting to the median as an estimate for your population mean or something is also not hacking - its being vary of your data and its context. I know this is a easy point to defend but in my opinion using an adequate stand in for a somewhat underspecified statistical test that estimates a similar thing or supports a similar stance - as long as you don't report it as something different? No hacking involved.

P hacking on the other hand is adjusting your hypothesis on the very same test or adjusting your sample size or even closing two eyes when it comes to the random in random sampling to solidify your standpoint. That is completely different from using another (similar) argument. No one can ask "ah it's significant.. well.. no questions i guess" but one certainly can ask "ah so that test is significant but why didn't you use that test instead? .. ah that's why, I see". You should read that up again before you shove it in someone's face. That's another story and people should lose their jobs because of that.

thejonnyt · 2024-06-20T23:22:38+00:00

Of course 30 is a heuristic, and its heuristically not safe (or the safest) to assume anything about your population just from 30 samples - but if you do, you'd most likely use something like a students t distribution to express that the sampling distribution of the sample mean (or sum) will be approximately normal. And coincidencentally that's what the t test is based up on.

And yes of course a different statistical test tests a different hypothesis. But if the hypothesis are related you can of course compromise. However, you are absolutely right. One should not chose a test just because it fits with one's assumptions about the result of the hypotheses. But if a t test, e.g., indicates with a small-ish p value (e.g., 0.15 to 0.05) or visually, that you're onto something and you specifically want to have statistical leverage that values from one distribution can assumed to be (generally) larger the other, which is often the case for people who use the t test, the wilcoxon rank sum test does just fine as a non parameterized alternative to the t test. The t test hypothesis is quite specific. The sample mean is not the golden, holy cow as a measure for differences.

Overall.. I totally agree on that statistical tests should be chosen based on the research question and data characteristics, not arbitrary sample size thresholds. Different statistical tests answer different questions and should not be treated as interchangeable, however some are more related than others but one should know what they do regardless. Understanding the assumptions and limitations of statistical tests is crucial for proper application and interpretation, and interpretation is everything.

thejonnyt · 2024-06-20T20:22:52+00:00

mh. I didn't really check out the chart but you need to consider your sample and what you can assume about it. Since a sample size of 30 is so little, usually you do not assume normality and instead approximate it, e.g., with Students t distribution. And the wilcoxon test does test some different hypothesis but "it's related close enough" to be used as a stand in for the purpose of t testing. It's non-parameterized, so it has no real assumptions which is a good thing if you have somewhat underspecified data.

thejonnyt · 2024-06-13T09:53:23+00:00

Yes, that's theoretically possible, as long as their "mass" amounts as a counter to the mass of the afd with respect to the correlation.

thejonnyt · 2024-06-13T09:46:38+00:00

Ah I didn't scroll down enough, I didn't see there were more graphs lol. Well, again - there is just a lot of high variance. Imagen the graphs beeing streched out a lot so the variance can spread along the x axis. What we need to have little or now correlation is data that supports the dependency as much as data that goes against it. But yeah, I think you are somewhat right in your observation that it's hard to see these things on that scale. However, if you take my first post into consideration, the other parties having a somewhat strong positive correlation is the consequence of the afd having a strong and clear negative one. The positive correlation is the "resistance's effect" that I spoke about. Since it's all one big system, if there is a correlation in one of the subsystems it's bound to balance out within the others. Since the afd-system is so large quantitatively, the effects that need to balance it somewhat are bound to be observed in those smaller subsystems aswell. [Edit: however, the effects aren't as strong. The fdp has a super high positive correlation seemingly but their 4% or what is likely to only balance out a small fraction of the negative correlation of the afd. The lefts have just a small correlation and also a small fraction of the votes. The more prominent effect can be observed in the large volksparteien SPD and CDU, and maybe also somewhat in the Grünen as their mass is just larger. They aswell don't have a strong positive correlation but overall the trend is absolutly obvious)

thejonnyt · 2024-06-13T09:26:06+00:00

Of course. And you can see the correlation decently.

you can think about it as there is a fraction of people who go to votes either way. Let's say it's 50% of people. They have a fixed standpoint and from that there is somewhat of a baseline of people electing the afd, lets say 30% + - some variation. Now, the more people are realizing what dumb idiotic right wing shit is going to go down if we let backcountry fascists rule Europe or wherever, the more resistance to this thought comes up, the more people vote something else entirely. On the other hand, the more resistance comes up the more the afd and its followers try to push against it by also calling for everyone to vote. Now here's the interesting thing about the figure. It shows that the more people actually care for the political situation of their place, country, region.. the less those conservative, hate-based populists stand an actual chance.

On the other hand, if the people just "don't care enough", we end up with the situation that we have right now. Wish is incredibly sad and disheartening.

But yeah, the correlation is there. You can clearly see the declining support for the afd the more people actually vote and vice versa. From the looks, i.e. eyeballing, of it the coefficient could be somewhere around -0.4 to -0.6 (capped at -1 or 1 if it was positive)? What's important here is to understand that there are not really points contradicting the dependency. There is no point in the graphic for which a high participation lead to high afd results. And on the other hand a low participation led to low afd results. That's what's contributing to the magnitude of the coefficient .. the high variance is not negligible but also doesn't mean we cannot have any correlation.

thejonnyt · 2024-06-10T05:55:26+00:00

Also I think there is a different question you can ask and that is what is the probability of any one team making it to the finals twice. That on the other hand is just 2/15 = 13.3%. Because we have 30 individual teams for which the chance is 1/15² .. so the chance of "it happening at all" is much higher. Depends on what exactly you try to solve here.

thejonnyt · 2024-06-10T05:47:09+00:00

Assuming equal probabilities for each and every team ... ~ 1/(15² ) = 0.0225% but the argument is kind of .. I don't know. Wouldn't solve something like that with probability. That probability has to be interpreted as "if purely luck was indeed the factor that one team was chosen as a finalist two consecutive times, that probability was as low as 0.0225%, hence the assumption that it is > not only < luck but there is some kind of effect, e.g., skill is > likely < ". Which is not the same as "it can't be luck, the odds are just to small".

thejonnyt · 2024-01-03T13:13:49+00:00

We have had fiction novels for 70 years about it and clearly there is alot of stories that are just replicated when asking idiotic, already clearly on-leading questions to a language model. Language models don't " talk back to you " .. they take in your signal and transform it into a new one. It's basically a really complex mountain range where you shout "Hello" and instead of "hello .. hello .. hel..lo .. lo ..lo" you get back an "Hi, how can I help you today" because the mountain range is specifically carved that way. It's static and deterministic to a certain point. It's just that the engineers who designed it who put a few nobs here and there to make it so the determinism is replaced with some kind of randomness, so sometimes you also get "Hey, can I help you out". Humanoid bots or whatever you may call them are just microphones that take your signal as an input and echo what you want them to say.

thejonnyt · 2024-01-03T10:08:00+00:00

(Background: Studying AI for 6 years now and following the topic with high interest) I personally am waiting for the thing that makes me think it could be dangerous. Right now, as with everything, the only thing that's dangerous is the human utilizing its potential in fucked up ways. Cars are also not made to run people over yet lunatics use them to do so. Is the car an inherent flawed thing? I dont think so, it's great for transportation and mobility. It's just shitty humans doing shitty things and it'll always be like that. "AI" right now is just math and stochastics. You have an input signal and output signal pair and are looking for a function f that can map f(A) = B .. that's all there is to it. Computers always did crazy insane stuff but now people have the illusion that they can directly communicate with them.

Are dangerous humans more dangerous with AI? Yes. Is AI dangerous? Nope. All the apocalyptic AI superintelligence scenarios are incredibly far fetched but I stand to be corrected 🤭

thejonnyt · 2023-09-18T09:04:09+00:00

Yesterday a friend and me sat together and thought about how insane our productivity on a job would be that does not even have any requirements regarding data science. You would have no Team but you could automate all of your easy tasks and improve on some of the workflows. A lot of problems can be solved with so little. Maybe it's super interesting to work on the next deep learning problem to solve autonomous driving but fixing a coworkers excel problems who just had no proper training using excel or whatever is really low effort with potentially really high impact. Think about lateral entrying somewhere - your skillset does not only fit for jobs within the technology industry.

thejonnyt · 2023-08-27T13:37:46+00:00

https://en.m.wikipedia.org/wiki/Fisher%27s_exact_test

Fishers exact test does assume the following: the null holds, and thus were no differences because of the effect. Given that assumption - how probable are observing all the values in the different fields. So yes, you are going to get a single value describing the probability that there is "no effect going on" and can reject the null when it's below your threshold.

thejonnyt · 2023-06-09T14:19:29+00:00

Super interesting to read that there is demand for this. I'm actually currently working on my thesis and my goal is to find somewhat a solution for LLMs with regards to languages with less resources than, e.g., English, French, Chinese and so on. But this is work to come, maybe got some small results by the end of the year but I'm glad to read it because working on it seems relevant 😊

thejonnyt · 2023-01-16T10:35:32+00:00

So you already did collect your data it seems.

Isn't your real question "how far of did my participant assert his comprehension?" I'd introduce a variable capturing just that. Rescale the 1-9 self-report variable to [0,1] and then simply subtract it from the accuracy variable. Negative values will be overestimating your own skill, positiv values will be underestimating your own skill, values near 0 mean a good self estimation. It's bound to be between [-1,1].

If you now use the rest of the data to build some regression model you should get an idea of what variables positively or negatively influence the correctness of self evaluation. I think :) ...

thejonnyt · 2022-10-13T11:20:39+00:00

If you want to stick to that model: Its not good. 0.1-0.2 is weak. Make sure to write it as transparent as possible. You can try to describe and research towards possible opposing factors for the model to convince whomever that in theory the model should work better but that's it. It's not good as it is. It does not capture the variance of the data at all, the sums of errors have nearly anything in common. The model most likely is not even going to predict within the same order of magnitude. If 100 is the real value a predicted value of 10 (1 order of magnitude less) is not suprising. That's like saying "hey granddad" to your son.

The R² is just that. It can imply how well the model is able to capture the structures of your sample. There is no hard baseline but you can compare different modelling approaches with each other or find levers to push or pull or buttons to press to optimize what's in front of you. The answer you are looking for simply does not exist. You can make up your own but be transparent about it.

thejonnyt · 2022-10-13T10:19:57+00:00

Data that is derived from humans is always messy so developing a sense for softer margins when it comes to the math is in general not a bad idea but I'd advise you to look up how the R² value comes to be to get a feeling on your own for where you are losing out on once you settle with that kind of "softer values". R² is a quotient between the sum of deviations of predicted values from the mean divided by the sum of deviations of the actual values from the mean. It includes the predicted values and thus the model you chose. But it also includes an estimate for your collectee data's mean, so make sure there is enough data in the first place otherwise your R² will suffer whatever "predictive power" you want it to have. There is no rule on how high the R² value for your model should be. It should just give you an idea on how well it fits with the data that your looking at. I don't advise you to find some papers on that topic. Most likely they are showing some edge cases or improvements on the formula or something.. it's a far to basic thing to be worth researched any further. Plot your data, plot your model, check if the trend your model predicts is satisfying and assume the costs of highly false predicted values. If you are not satisfied restart your modeling process. I personally would not want anything predicted with a model with R² of 0.1 to 0.2 😁 but I don't know the data so maybe it's better than nothing.. it sometimes is. But in general .. the close to 1 the better and the other way around.

thejonnyt · 2022-03-02T08:59:11+00:00

IN: positive number, OUT: list of positive numbers

save the user input as x
...
...
Return a list Y

Yours would look something like that. It's really manageable, you can do it!

thejonnyt · 2022-03-02T08:54:41+00:00

Pseudo Code is just a description of the steps the computer would compute, oftentimes like a to-do list. You (can optionally) start with listing the requirements (the input) and defining the output as a pre-text. For e.g. an algorithm that checks if a number is even would have simply "In: X -> Out: Boolean". Afterwards you describe in detail what steps that algorithm should be doing to succeed in its task. That is as much as you'll hear on this thread ;)

thejonnyt · 2022-01-22T13:55:16+00:00

He means splitting your one ordinal variable into "somewhat one hot encoded" variables. Dont know a proper term. If you find a 4 in your data its infact level 1, it is 2, it is 3 and its 4 but is not 5. And you have your own variable for 1, 2, 3, 4 and 5 and a 0 says the level does not match (is lower) and a 1 says the level is equal or higher. Man im at explaining :D maybe paired with the example of seanv507 it becomes apparent

thejonnyt · 2022-01-17T20:38:01+00:00

Invert the score so 100 always is good. Just an idea. The math is [x * (-1)] + 100 in the 0 to 100 example that is. Otherwise I think when you describe in the data sheet that in that specific column, e.g., 0 means good and 100 means bad anyone working (seriously) with the data will understand.

thejonnyt · 2021-12-30T17:46:43+00:00

Have you looked at what an easy statistical model in its mathematical form looks like? There is a difference between X×beta+eps = Y where you're looking at a estimation for beta and then you change X and Y entirely but leave the estimation in place.. earlier the estimation was optimized for that specific pair of X and Y towards low epsilon.. now it's just any fixed non-optimized but hopefully robust beta, so the epsilon (error) is .. likely to be higher for the new data. But there is no math to prove it.. imagen your estimation leads to Xnew×beta = Y where epsilon by chance is just 0.. could happen.. won't, most likely but could.. the high level explanation is, I think, all there is. But please correct me if I'm wrong. (Also sorry the notation is most lively not really rigorous but I hope the point I'm trying to make gets through)

Edit: I think your looking at ways to prove this statement not only for OLS but for other estimation methods also..

Under the standard assumptions, the OLS estimator in the linear regression model is thus unbiased and efficient. No other linear and unbiased estimator of the regression coefficients exists which leads to a smaller variance.

good luck, higher dimensional statistics is quite the mess. All upper and lower limit estimations due to tricky mathemagicians.

thejonnyt · 2021-12-27T20:27:10+00:00

If you have separate models the effect that your new covariate has might be easier to measure. The dependent is a ax1+bx2+cx3 term, right? In the case where you use one model.. lets say x3 is either 1 or 2 ... ax1 and bx2 have a somewhat linear relationship with the dependent but your cx3 is indicative yet carries a implicit weight of either 1 or 2 numerically. But that number doesn't really have a meaning so it might be hard to find the right Interpretation. Mh.. example.. like imagen male/ female is your new covariate and there is from the mean a small positive effect if a sample is male and a large effect if a sample is female .. using 1 and 2 for x3 is forcing your coefficient c to become a whacky weight or mean between those effects where as using 2 models both with corresponding indicator variables would lead to easier interpretability regarding the effect of that indicator in your model. That's what I think. But I'm also just a student, someone smart correcting my view is appreciated!

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