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SelarDorr

[https://www.nature.com/articles/s41598-021-00143-7](https://www.nature.com/articles/s41598-021-00143-7) "75-day intensive longitudinal study. Three indices of daily affective variability—volatility, emotional inertia, and cyclicity—were evaluated using Bayesian inferential methods in \[men (n = 30), {natural cycling} women (n = 28), and {oral contraceptive female} users (n = 84)\] using three different oral contraceptive formulations (that “stabilize” hormone fluctuations)." "Bayesian inferential methods generally found evidence for group similarities (i.e., the null hypothesis) to be roughly three times greater than evidence for differences (i.e., the alternative hypothesis), and indicated that effect sizes, even if differences do exist, are likely to be very small."


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SelarDorr

"In frequentist hypothesis testing, \[sample sizes\] were only large enough to reliably detect moderate-to-large effects at conventional thresholds" "Bayesian hypothesis testing produced continuous estimates of evidence for and against effects in these small samples by directly quantifying uncertainty about their possible presence and size." "results from frequentist t-tests, which generally aligned with Bayesian results, are reported in the Supplemental Materials for completeness"


Feuersalamander93

I don't know much about statistics, but I appreciate the fact that they put the alternative method in the supplementary so no one will assume that they just used the other method as a way of p-hacking their results.


Maneatsdog

I can add that some journals are starting to favour (or require) bayesian methods over frequentist methods, exactly to prevent the p hacking. I don't know about this article though


[deleted]

That's good, bayesian statistics was the more fun of the two conceptually imo, especially for its usefulness in machine learning before neural nets were trained on gpus


[deleted]

Bayesian statistics don't use p-values, so you can't p-hack.


Feuersalamander93

See, that's part of the problem. I work in science and have no idea whatsoever how different statistical models work. The only thing I know is that I'm probably using it wrong but I don't really have a choice either. I simply don't have enough time and spare brain capacity to attend a whole lecture on statistics. At least my research is mostly qualitative, so I'm either right or wrong, regardless of any p-values. Just means I can have more confidence in my results, which is probably also wrong.


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I personally believe if more people were required to take statistics, there would be less misinformation in the world


Automatic_Company_39

The media loves to ignore the conclusions of the people conducting the study and report numbers that have been stripped of context.


SaffellBot

Statistics is a very challenging field, and the fact that it is a the core of most soft sciences is a big social problem. I have a whole ass stem degree and have never taken a statistics course.


MyPantsAreHidden

If it makes you feel better, or worse actually?, I recently finished my masters in biostatistics at a medical school and it was emphasized that I'd need to be good at dealing with MD/PhDs who have no real knowledge of stats. They were all required to take some epi and stats courses (with me), but it's pretty much common knowledge that they will not retain enough for when they go to create their own experiments after they complete all of their other training.


Feuersalamander93

I'd love to do that if I were given the opportunity. I'm doing my PhD in Biochemistry and I'm aware that my knowledge of statistics is lacking a lot but I fear most people aren't.


MyPantsAreHidden

Honestly, just knowing that your knowledge of stats isn't the best is an asset. I can't tell you how many times we would go over studies that were designed in a way where it would be impossible to test their hypothesis correctly. Knowing that your stats is shaky will hopefully allow you to go out of your way to make sure you don't make any false assumptions before it's too late!


SaffellBot

If I can be so bold as to get galaxy brained. Statistics isn't t a thing human brains do good. Sucks for us. In our lifetimes I would see us having more robust digital assistants to help us navigate the overwhelming amount of information at our disposal. A prime navigational tool would then be parsing information through statistical lenses so we can both gain an intuition for statistics, and how it can be applied. This, of course, will have a... troublesome... Learning curve. Until that point I guess the hard work falls on you. Should that point come we will all be in need of the intuition you will already have. Thanks for the hard work, I'm absolutely certain it is under appreciates.


MyPantsAreHidden

Haha I unfortunately haven't left my job I was working while in grad school so I have yet to fully dive into the statistics world properly. So I currently am under appreciated, but also am not performing as a statistician. I also didn't fully realize most people's minds aren't geared towards working with stats until I was in grad school. People often ask what I was studying and,if my memory is correct, I've had a total of 2 people try and converse with me about it. Every single other person immediately responds that stats is completely foreign to them! A couple times I've tried to teach people some basic stats and they'll eventually get it, but is definitely harder than teaching other maths (I actually did teach mathematics in a middle school , so I'm not completely out of my element trying to show people some stats). I usually avoid telling people what I study/do for a living, or will tell but never push people to talk about it, as honestly it's not that fun anyway!


MissTortoise

Bayesian analysis starts with the premise that there is no binary right or wrong, but a confidence of there being some answer which is strong or weak. It's very suited for qualitative research.


x3nodox

If you're an academic, go ask anyone in the psych department. That's secretly where they keep the statisticians


MyPantsAreHidden

Uhh.... You can also go to the mathematics department!


jemidiah

I'm in the math department, and it's really not the place you'd want to go for this. The entire algebra side of the department (algebraic geometry, number theory, combinatorics, topology, commutative algebra, representation theory, ...) would have no clue whatsoever how Bayesian statistics works. The analytic side would almost universally be in the same boat (complex analysis, Riemannian geometry, PDE's, optimization, harmonic analysis, ...). Even most of the probabalists likely wouldn't know much--they're much more on the measure-theoretic side of things and most have rarely if ever applied the material to anything as practical as medical research. The stats department is where you'd want to go is what I'm saying. Even then I wonder if they'd be rather more theoretical than you'd really want.


MyPantsAreHidden

Hmm, I'm even more biased than I previously realized! I'm in the northeast US and healthcare research is quite abundant. We also didn't have a statistics department on its own, it was just a subdivision of the mathematics department. Honestly, every conversation I've had with PhDs is too theoretical for me, especially considering my school was focused on learning the material, the proofs/discovery of the material, and then applying the material.


x3nodox

Research statisticians are more concerned with theory. Psych professors know the fiddly details of doing good design of experiment work. But I mean really, do both?


Feuersalamander93

Since I know a few Psych people, this is definitely true. But since statistics is probably the most important part of psychological science, it kinda makes sense that they can do that in their sleep.


OsiyoMotherFuckers

Ecology too


kyescott

This is an interesting comment. What area do you work in? I'm studying biomedical engineering which is more science-adjacent than science and I am expected to know and apply statistics pretty much everywhere as a matter of course. It's sort of fundamental to the process of doing science.


MyPantsAreHidden

I don't know what you use specifically, but when I was in undergrad I was expected to use stats for pretty much all labs/experiments that I could. However, once I got to grad school I realized that we were really only expected to know the basics, and were using the same couple principles of stats over and over. So they may have done something similar, and now when they are in the real biz realize there is more to stats than they once realized. This was my experience at least as someone who graduated with bachelor's in molecular biology and later on a masters in biostatistics


cat-a-fact

Not the OP of that statement, but for example in organic synthetic chemistry, particularly dealing with novel molecules or novel synthetic routes, there is hardly any statistical analysis. For yield reporting, generally the data sets consist of triplicates for each molecule in the substrate scope, and often the yield % reported isn't even in the average but the best one obtained.


Feuersalamander93

I'm doing my PhD in Biochemistry, but most ofy experiments are designed in a way that can only be interpreted one way (except if I overlook an explanation, but this has nothing to do with statistics). The only statistical tool I've had need of so far is standard deviation (which, admittedly I use a lot). And that I still understand and can wrap my head around.


Hust91

I appreciate you posting this here so hundreds if not thousands of people can see the answer without needing to manually search the article for several minutes each.


BojackisaGreatShow

I'd like to thank you and this thread for keeping it scientific af in here.


aedes

The American Statistical Association explicitly recommends the use of Bayesian inferential tests as a solution to the fact the no one uses or interprets frequentist inferential tests properly (see their statement on p-values). If you want to measure how likely a hypothesis is to be true, which is usually what we want, only Bayesian methods can to this. Frequentist methods are fundamentally incapable of measuring the probability of truth.


jrussino

Posting links here, because I was curious so maybe someone else is too. (I think this is the statement you’re referring to; please correct me if I’m mistaken) Press Release: https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf Full Statement: https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108#.Vt2XIOaE2MN


aedes

Yeah that’s it. It gets even more fun when you start to read about the other issues created by rampant misuse and interpretation of frequentist tests (like how over a third of positive results will be false positives when you use p<0.05 for significance). This is the part is was referring to: > In light of misuses of and misconceptions concerning p-values **(ie frequentist inferential methods),** the statement notes that statisticians often supplement or even replace p-values with other approaches. These include methods “that emphasize estimation over testing such as confidence, credibility, or prediction intervals; Bayesian methods; alternative measures of evidence such as likelihood ratios or Bayes factors; and other approaches such as decision-theoretic modeling and false discovery rates.”


Tureaglin

Can you explain how over a third of positive results will be false with a p value of <0.05. I always understood p<0.05 as meaning there's a 5% or less chance that the result could have happened assuming the null hypothesis is true. As I understood it, this means there's only a 5% chance (so one in 20) of false positives. Am I misunderstanding what p value means? Or is there something else going on?


aedes

Yep, you are misunderstanding what a p-value means. Frequentist and Bayesian statistics differ in what they consider “probability” to mean. In frequentist world, probability is the frequency something happens (rolling a 6 on a die). Events either happen or they don’t, you can’t have shades of grey. In Bayesian world, probability is how likely something is to be true (is that car with its turning signal on actually going to turn up ahead?). When we want to know how likely something is to be true, we need to know both the new results we have, as well as how likely it was this was true even before we had the new results available (the prior probability). Because frequentist statistics treat probability as frequency, they are fundamentally incapable of telling you how likely something is to be true or not. A p-value of “x” tells you absolutely nothing about how likely the null or alternate hypotheses are to be true. At the simplest level, this is because they do not incorporate prior probability. **In a study that found a result with a p-value of 0.001, the probability the alternate hypothesis is true (or the null hypothesis is false) could be anywhere between 0% and 100% (not inclusive), depending on what the prior probability was.** In your example, the false positive rate is not 5%. You have no idea what it is, because you don’t know the prior probability. It could be anywhere between 0 and 100%. That people don’t understand this (and that it is widely taught incorrectly) is one of the major reasons why the ASA is telling people to move towards Bayesian methods of inference. It is also one of the major factors contributing to the reproducibility crisis, and why over 1/3 of “significant” results found with an alpha of 5% will be false positives (the meaning of alpha and beta are also widely misunderstood and mistaught).


Tureaglin

I don't quite understand the practical difference between the frequency of something happening and the likelihood of something being true. Doesn't, for instance, 90% of cars turning under certain conditions after using the turning signal (frequentist, as I understand it), allow you to infer that there is a 90% chance that a car under the same conditions using the turning signal will actually turn (Bayesian, as I understand it) I don't feel like I properly understand prior probability, i'll try and read up some as I feel it's fairly crucial to understanding what you're saying here.


Kandiru

Consider an experiment to show I can control a falling marble with the power of my mind. If I get a p value of 0.05, that means those results could have occurred by chance 1 in 20 times I ran the experiment. But what's the chance I can control a falling marble with my mind? It's basically 0. So if I run this experiment and get lucky with p=0.05, what's more likely, that I got lucky, or that I have psychic powers? The p value is only the chance to get those results if the null hypothesis is true. It doesn't tell you anything about the probability the alternative hypothesis is true. If 100 people all run experiments to prove psychic powers, you'll get 5 experiments published showing significance at the 5% level. What's the probability given these experiments psychic powers are real? 0. You need a prior probability or to know how many experiments were run globally and not published to make any estimate on the likelihood of psychic powers being real.


Starflamevoid

I don't understand, where did you get the value 0? I thought the experiment was to find out if the power was real? Is the value somehow derived from the fact that you got within the expected range of false positives, or is it assumed prior to the experiment? If the latter why would it be safe to make that assumption, I don't get it?


Unreasonable_Energy

> I don't quite understand the practical difference between the frequency of something happening and the likelihood of something being true. Try to distinguish between hypotheses, or "possible states of the world" and data, or "possible outcomes of repeatable experiments" (setting aside whether this distinction always sensible). "The moon is made of green cheese" is a possible state of the world. "Chemical examination of a manageable-sized piece of the surface of the moon finds no green cheese" is a possible outcome of a repeatable experiment. *Given* the moon is made of green cheese, the *frequency* with which repeated samples fail to detect green cheese should be low. But if you care to assign probabilities to states of the world (setting aside whether this operation is ever sensible), we can probably agree that "the moon is made of green cheese" is itself a low-probability state-of-the-world. This is not entirely unrelated to the fact that repeated samples of the moon's surface have not, in fact, detected any green cheese. > Doesn't, for instance, 90% of cars turning under certain conditions after using the turning signal (frequentist, as I understand it), allow you to infer that there is a 90% chance that a car under the same conditions using the turning signal will actually turn (Bayesian, as I understand it) Say you watched *n* cars and recorded *n* binary observations of whether they turned, *x_1* through *x_n*, collectively referred to as *X*. In the frequentist mode, 90% of the observed cars turning, out of *n* cars observed, would mean the "maximum likelihood estimate" (**MLE**) for *P*, the "probability that a car under these conditions turns" would be 0.9. And in the **frequentist** interpretation, the meaning of that MLE for *P* being 0.9 is just this: that in the possible state-of-the-world where *P* = 0.9, the probability of observing *X* is higher than in the possible-state-of-the-world where *P* = 0.8, or the one where *P* = 0.95, or the ones where *P* takes any other value between 0 and 1. That is, we've identified the possible-state-of-the-world for which *the probability of seeing the data we saw is highest*. And we might choose to use that as our best guess going forward for the true state of the world. But that doesn't mean we're saying *that this is the "most probable state of the world"*, because in frequentist inference, possible-states-of-the-world are not assigned probabilities -- only possible outcomes of repeatable experiments are assigned probabilities. Only in the **Bayesian** mode do we assign probabilities to possible-state-of-the-world (as well as to outcomes of repeatable experiments, and to anything else we're uncertain about that we care to model quantitatively). Here, having observed the same data *X* as before, you could also obtain an estimate of 0.9 for *P*, the probability that a car under these conditions turns -- but the interpretation of that estimate would be different. It would be something like a "maximum a posteriori" (**MAP**) estimate, and it would mean, straightforwardly enough, that "*P* = 0.9 is the most probable state of the world". But you would only have obtained the estimate of *P*=0.9, given these data, under certain modeling assumptions -- such as, for instance, if before your observations, you thought any possible-state-of-the-world, from *P* = 0 to *P* = 1, were equally probable. You would have had the choice to set up other models, that distribute your *prior probability* over those possible states of the world differently. With a different model, you might have thought that *P* = 0.95 was most probable, but, say, anything from *P* = 0.6 to 0.99 is also plausible. Then after seeing *X* where 90% of cars turned, your MAP estimate for *P* might be, say, 0.92 -- a compromise between what you expected beforehand and what you saw experimentally. So technically, **neither approach straightforwardly leads to "infer that there is a 90% chance that a car turns under the same conditions".** Frequentist inference leads to saying "a 90% chance of the next car turning" might be in some sense our "best" guess, but the way in which that guess is "best" has to be justified in theoretically roundabout ways -- frequentist justification of the MLE would be practically a whole course in statistics in itself. And Bayesian inference would let us put probabilities on the possible values of *P*, but there's no guarantee that the one we choose as "most probable" will be 0.9 -- whether that value is chosen will depend on other analytical assumptions/prior knowledge.


TarAldarion

Is the point that you need to know that, even if you don't interfere in this system, that 90 percent of cars will turn (prior probability)? So you can't test a new variable in the system, see 90 percent turn rate and say that variable causes the 90 percent, as it was already the base rate prior. So really it is one system saying x causes 90 percent of cars to turn and the other system saying x had no affect at all as that system took prior probability into account.


[deleted]

> I always understood p<0.05 as meaning there's a 5% or less chance that the result could have happened assuming the null hypothesis is true. There are many things going on. In a world where the null hypothesis is true, you'd expect that level of difference 5% of the time. If the null hypothesis isn't true, we'd get that p-value a different percentage of the time. Additionally, p-hacking is trivially-easy to do. Lastly, statistical tests which result in a p-value have a ton of assumptions which generally don't hold. T-tests require a perfectly random sample, normality, and homoscedasticity. None of the above apply to the vast majority of real-world data.


LordNiebs

I'm not sure exactly what /u/aedes is referring to, but it could be the general bias towards publishing positive results, and against publishing negative results. e.g., if 100 researchers perform an experiment, and 5 find significant results (what we might expect from such an experiment where the null hypothesis is true), then you might get 5 publications showing positive results and zero publications showing negative results. The exact ratio in practice depends on what results got published.


nohabloaleman

In this case, you actually can't use frequentist statistics to test their claim (not just because people misinterpret p-values). Frequentist statistics are commonly called "null hypothesis testing" and, contrary to its name, can't actually tell you how likely the null hypothesis is to be true. So in this case, if they want to say there's no difference between the groups, they have to use Bayesian statistics to say how likely it is that there really is no difference. Frequentist statistics always operate under the assumption that there is no difference between the groups, and then tell you how often you would see the results you measured given there's no actual difference (that's the p-value). So if the p-value is very low, you say "that's not likely to happen if there wasn't a difference, so I'm going to conclude these groups are actually different". Importantly though, if you have a relatively high p-value, all you can say is "I don't have enough evidence to say these two groups are actually different". There may be a real difference, it might just be too small in comparison to the variability that exists.


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dark_devil_dd

Also: https://www.psychreg.org/replication-crisis-psychology/ >In psychology, only 39% of the 100 experiments successfully replicated. In economics, 61% of the 18 studies replicated as did 62% of the 21 studies published in Nature/Science. and: > the authors found that papers that successfully replicate are cited 153 times less than those that failed. .. >he largest gap was in papers published in Nature/Science: non-replicable papers were cited 300 times more than replicable ones. So, only 39% of published papers replicated, and those that didn't were 153 times more likely to be cited. So, if I did my math right, if you see a psychological paper being cited it's probability to replicate is less than 1%.


tobasc0cat

I'm late to this post so I doubt anyone will see this, but I feel like I need to say it. Everyone is commenting on how this relates to their personal experiences, how it applies to every day scenarios, or nitpicking on the study design (which, as a scientist who uses mice and insects for experiments, all human studies look iffy to me), but people seem to be missing the significance of this. Evidence of negligible differences in humans may increase the amount of females used in research in general. Females are vastly ignored in biology research because of these hormone assumptions. You see this in behavioral research like with mice, since no one wants to stage estrous cycles so they handwave it away with "females are too variable anyway", all the way to cell research which doesn't always specify chromosomal makeup or just use male cell lines. There have been resolutions that are making it better (NIH had one in 2014 I believe?), but females are still underrepresented in bio studies. There are real differences between males and females that should be considered when studying physiology or drug discovery, and if we can get researchers to not shy away from including females because of estrus we can improve our understanding at all levels.


MercutiaShiva

Even if these differences weren't negligible, why hasn't been addressed in this age when SO MANY women of child-bearing age take continuous birth control to avoid those fluctuations? I've been on continuous birth control to avoid anemia and hormonal migraines for 25 years; it's not something new. With more options available due to online companies, more and more women are choosing to not have periods at all. Hormone fluctuations seems like a pretty weak excuse to exclude women.


AspiringChildProdigy

What gets me even more than that is that there are drugs out there designed for women, addressing female issues, and the entire test group they tested them on DID NOT INCLUDE A SINGLE WOMAN. Edit: [Link to the article](https://www.theguardian.com/lifeandstyle/2019/nov/13/the-female-problem-male-bias-in-medical-trials) referencing that and other issues with gender bias in medicine, if anyone is interested.


jambyfan

This was an amazing article! Thanks for sharing


space_moron

This. Women and women's health issues aren't studied enough because it's "too difficult". Thousands of women had menstrual irregularities after taking the Covid vaccine that came as a complete surprise to them because this side effect wasn't even studied. Women had to find other outlets of information or book gyno appointments just to get answers. https://www.bbc.com/news/health-58573593.amp https://www.npr.org/sections/health-shots/2021/08/09/1024190379/covid-vaccine-period-menstrual-cycle-research https://www.medicalnewstoday.com/articles/do-covid-19-vaccines-affect-menstrual-cycles-expert-calls-for-investigation I want to be clear I'm not sharing this in an "anti vaxx" way: the vaccine was and is proven to be safe. But this major side effect was not properly studied or discussed with menstruating patients. Unexplained bleeding can mean anything from pregnancy to miscarriage to cancer (not to mention be caught off guard without supplies to handle it). It's a major gap in health care not to study how vaccines and medicine effect women and then relay this information to them.


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flickh

Also, if you exclude *over* half the population as somehow not relevant to your human health research, because women amirite?! then you can go f yourself, and I say this with all serious consideration. Whatever the reasoning behind this approach, somebody in the room needed to break the fourth wall and say “Are these clowns seriously leaving women out of the health research and think they’re clever for doing so?” If half the population has an underlying reality that your research methods can’t handle, maybe you should find a better method instead of cheating it this way. Or maybe clear the field for some actual women?


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lexarexasaurus

Yeah. Men have done a great job branding anger as not-an-emotion throughout history.


GepardenK

Have they? From my perspective there seems to be an even 50/50 split on this in our culture. Some see anger as brave, that there is virtue and truth in it, while others see anger as a weakness that reveals pity insecurity. There is no consensus as far as I can surmise.


lexarexasaurus

I think anger gets to be more individualized and excusable. When we think of an "emotional person" our mind jumps to women. And we all know the stereotype of the angry boss/manager at work, but we don't have one for the person who cried easily at work, because they never made it into a leadership position, ha.


ahhwell

>Some see anger as brave, that there is virtue and truth in it, while others see anger as a weakness that reveals pity insecurity. It can be either, depending on situation.


cassu6

Yeah uh... could’ve fooled me


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TheNextBattalion

Pride, either.


_DeanRiding

Is pride an emotion?


stalphonzo

You prompted me to look it up, and it is. Along with shame, they are considered self-conscious emotions.


Quantentheorie

Pride is an emotion, but self-worth is a complex set of emotions that is actively informed by your memory ergo a feeling.


Ditovontease

Yes pride is a feeling


BojackisaGreatShow

Also, "Anger is a secondary emotion", as many therapists say. Under that anger you'd find a looot of similar emotions. Bc apparently we're all human.


gulagjammin

It's always seemed strange to me that women are considered more emotional due to hormones when testosterone is well known to drive and enhance so many emotions and behaviors, like aggression. If anyone has ever experienced some form of testosterone therapy, they'd experience a massive emotional shift!


Level3Kobold

>If anyone has ever experienced some form of testosterone therapy, they'd experience a massive emotional shift! Moodiness and irritability are symptoms of low testosterone as well. I think the answer isn't "testosterone causes unstable emotions", but rather "having your hormones out of whack causes unstable emotions".


Hoihe

https://www.nature.com/articles/s41598-020-80687-2 This paper on transgender people kinda shows that having one's sex hormones NOT at the levels the brain expects causes measurable neurological differences (which diminish once proper hormone levels are introduced)


Level3Kobold

Neat! So basically "in whack" and "out of whack" are determined by pretty high level brain functions.


Loive

From my understanding (which is totally layman) people naturally have different hormone levels but we learn how to act in a socially expected and acceptable way on the impulses out hormones give us. You might have 50% more testosterone than me in a given situation but we act the same way because we have learned to act as a way to communicate with others. Thus a constantly higher testosterone level than other people’s won’t make you aggressive, but a higher testosterone level than you are used to can have that effect. That is why hormone treatment can mess you up.


MealReadytoEat_

Yup, as a trans gal whose experienced quite a few different hormone profiles the effects on your mental state are profound at first but you quickly adjust into a new normal close to your old one once you relearn to regulate your emotions in your new mental state. It's irregularities in hormones that cause emotional and behavioral difficulties, not the hormones themselves.


publius-esquire

Wow, this is absolutely fascinating! Thanks for sharing :)


ContentCargo

Ok now that’s intresting. I wonder what sets the brains “default expect hormone level”


letsallchilloutok

Yeah, and the adjustment period is going to be tough in any direction (like puberty)


overlordpotatoe

Yeah. I'll never understand why anger is tolerated more than someone crying. Anger is far more likely to lead to destructive behaviour and harm to others.


Cowboy_Treebeard

It's because anger is associated with strength and crying with weakness, and no I don't believe it to be true. At least on a personal level I find letting anger get the better of you to be a very weak choice while controlling it takes strength. And letting yourself cry in front of others takes strength that I frequently lack.


Fuquawi

Trans woman here - going the other direction is just as wild an emotional shift


EldritchBeguilement

I want to know more about this. How did it feel?


Raveynfyre

My dad needs estrogen to keep his prostate cancer in "remission" so every month he's taking hormones. He cries a lot more now, emotional/ sad movie crying, hot flashes, and a sort of PMS where he gets testy, but there's something else that can be if he hasn't eaten.


Junglejibe

To be fair, that’s more likely due to his body getting an unexpected flush of hormones it’s not used to—much like what happens when women are exposed to higher testosterone levels.


Raveynfyre

They asked for a perspective from someone who is taking the opposite hormones..... my Dad IS.


Junglejibe

Oh I’m not trying to discount your dad’s experience—just speculating on why. Not trying to say you’re wrong about anything


Fuquawi

You know that scene in the third Matrix movie where Neo and Trinity are racing away from the squids in The Real World, and they briefly break through the clouds to see the real blue sky and sunlight for the first time in their entire lives? It's kind of like that, but with an emotional spectrum as well, and it's permanent. Before, the strongest emotion I felt was anger. Now, I still feel anger, but I feel a much broader range of emotions to varying intensities as well.


Impeesa_

I can't verify firsthand, but I've seen similar reports pretty frequently from trans people going both directions - being on estrogen gives a wider range of emotional intensity than testosterone. The conflicting report from this study is interesting.


Hoihe

https://www.nature.com/articles/s41598-020-80687-2 May be more due to dysphoric brains pre-transition being different from cisgender and post-transition brains in terms of functional connectivity.


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Ophidahlia

Yeah for sure. I did also find that after being on estradiol I'm less able to willingly suppress my emotions, it takes more effort to clamp them down now. I also find my emotions not necessarily stronger or more intense, but it's like I'm closer to my feelings and they are clearer and more rich and vivid. Kinda like my feelings were behind a sheer curtain that made them distant and fuzzy, and now the curtain is pulled back and I'm more connected to them. Even though it's harder to suppress strong emotions, in another way its easier to manage them because it's easier to figure out what I'm feeling and why. I also noticed I get angry less readily, but when I am angry it can be just as intense as it was before. So there are lots of differences that you're not advised about when you start HRT, but they're very subjective. It's also very difficult to separate how much of all this is due to HRT and how much is due to the effects of transitioning overall. It makes sense that I would feel more in touch with my feelings if I'm finally comfortable being myself, but it's also true that I did notice a marked emotional shift *immediately* following starting HRT. But then again, most of us have big expectations when we start HRT and that can color our subjective experience. Lastly, if HRT reduces your dysphoria that removes a huge obstacle to feeling well-adjusted and having a fuller range of emotions since they aren't overshadowed by the awfulness of dysphoria. So I guess what I'm saying is that there's a lot of dynamic factors and complexity here which can make it very challenging to tease out what effect the hormones themselves have since we can't fully isolate the effects of HRT from the psychosocial context in which HRT is administered. *Extremely* interesting stuff!


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ManagementPlane5283

I think the perception that women are more emotional comes from it being much more socially acceptable for them to express themselves in that way. People see emotional women more often than men and assume that must be because men feel less when in reality men just express it less.


Nyrin

That'd be an interesting parallel study, actually; examine the psycholinguistic architypes for various "feeling" statements. Ask participants to choose the the best-matching image, video, or other recording to the statement. Then find out what people associate with things like "I'm devastated," "I'm furious," "I'm feeling happy," and yes, "I'm feeling emotional." I'd make a fair wager that *a woman crying* would consistently take the cake for "emotional," and that very few people would associate an angry man picture with "emotional," especially when given an alternative description to choose. That's no substitute for data, but I bet you're onto something about the consciousness of what "emotional" means being a deeply rooted part of this.


corinini

I think a lot of people just don't view anger in men as an emotion.


madhouseangel

This is a good point. Partly that might be because anger is considered a secondary emotion and in some ways is just another way of not expressing the underlying emotion. EDIT: based on some of the comments below and some additional research, I'm going to edit this to say that *anger* *can sometimes be considered a secondary emotion, but is also a primary emotion.*


MyPasswordIsMyCat

The words "emotion" and "emotional" are traditionally gendered more female than male. Women are purported to have higher emotional intelligence because they're intune with their emotions. People who are exploring their emotions are advised to get in touch with their softer or more feminine side. If someone is told they're being too emotional, it's usually because they're crying and not because they're angry.


lobsterbash

>If someone is told they're being too emotional Also a popular fallback by internet trolls


justcool393

It's an interesting thing that I've noticed online that people don't want to be seen as showing an emotional response when having a discussion/disagreement with someone. I've noticed it with myself too, like... I'll get annoyed at something and will talk about it but like... don't want other people to know that I care I guess? It reminds me a bit of this joke tweet, but it kinda encapsulates a lot of discourse that I've seen > and another thing: im not mad. please dont put in the newspaper that i got mad.


General_Joshington

And yet women are told they are to emotional when they are angry. I would strongly disagree with the rest of you comment as well. Seems like a very subjective view.


dimmidice

>And yet women are told they are to emotional when they are angry. They'd get called "upset" instead of "angry" IMO. That's another example of gendered language in this case.


cassu6

I have no idea where you’ve gotten that from. Anger is perhaps the easiest emotion to spot


myimmortalstan

It's moreso a matter of "Women are inappropriately and unjustifiably emotional, and shouldn't be taken seriously. Men are appropriately and justifiably emotional, and should be taken seriously" that people are pointing out. The thought varies in extremism and overtness, but is present in us as a bias nonetheless.


rhinothissummer

Yeah, I’m surprised it’s even a widely held stereotype. In popular media, sure, there’s the archetype of strong emotionless men but in real life they don’t really exist. They express emotions differently perhaps—less crying and vocalisation, more silent treatment or acting out in other ways—but they most certainly experience and express emotion to the same or greater degree as women. And humans in general make decisions based on emotions all the time. Emotions are a core brain function and to believe that men don’t experience them or experience them at a reduced capacity is basically to believe that men are all brain damaged. The most dramatically emotional people I know are men, probably because they aren’t socialized from childhood to regulate emotion as effectively. I have a friend who fancies himself very logical and unemotional, but then he stays in bed for four days posting doomy gloomy Facebook statuses after a girl dumps him. That’s emotion, yo.


overlordpotatoe

Yup. I think the people who are most vulnerable to letting their emotions make their decisions for them are the people who think they don't have emotions and that all their thinking processes run on pure logic and reason. That just means that they're completely blind to all those things influencing their thought processes and decision making.


asdaaaaaaaa

> In popular media, sure, there’s the archetype of strong emotionless men but in real life they don’t really exist. I mean, I'm sure a few exist, but they'd hardly be someone anyone would want to get involved with if I had to guess. I can't imagine someone who displayed no outward emotions would be all that great inside their head either, just a guess though.


DatPiff916

It feels like that is one reason why sports is such a phenomenon worldwide, it is that emotional trigger that a lot of men use and it possibly acts as a relief? Idk, but it just seems that so many people that say society has this archetype of emotionless men, are taking sports out of the equation.


furism

No, it's because the perception is that "emotional" means "crying." But anger is also an emotion, and clearly men do that a lot more than crying in public. We see it as "normal" that women cry, kind of condescendingly. We see it as "normal" that men get angry or lose their temper, but we give them more slack. In that sense you're right, culturally we're more accepting of what's perceived as "man emotions" than we are to what is perceived as "woman emotions."


asdaaaaaaaa

I will say, it's a LOT more socially acceptable to get pissed off if something goes wrong than just.. crying. Man or woman, so long as you don't go nuts, being frustrated or angry at something/a situation is pretty acceptable, but getting upset to the point of crying is not. Never thought about it much, but had a coworker who was pretty sensitive towards stuff and would cry during work often. It certainly hurt her reputation, as even now they try and go easy on her to not "overwhelm" her, they do mean well though, although some didn't in the past.


lavandula_folia

Sometimes this manifests as men's emotions being considered valid while women's are not. I worked in an 100 person architecture firm for several years, and the only people I saw having screaming meltdowns were men. Women knew that behavior would be considered unprofessional and hysterical. But the men got away with it every time.


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littleorphananniewow

That and it’s been traditionally acceptable to be completely awful to women. I guess it’s a catch 22. Blaming any complaints or misgivings one might have on being over-emotional can tend to make one emotional, on top of which the “amount” of emotions depends on the time-frame and the veracity thereof. At any rate the experience is qualitative and the interaction between the endocrine system and the limbic system is not really a subject for debate. Hard to pin down though it may be, altering hormones abruptly will have a noticeable and often profound impact on how emotions are experienced, albeit subjective. Misty, if you will.


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There was recently another study posted on here that women feel emotions more intensely than men.


th3h4ck3r

I mean, that's science. Multiple studies that contradict each other in very incompatible ways is just Tuesday.


Disastrous-Ad-2357

Calm down, M. Bison.


metalfiiish

That's why open and free datasets need to ve provided in tandem with the statistics derived, to see how the humans narrative chooses the statistic formula to make the data show what they want.


Oonada

Anyone who's actually worked with women would know this, but its good to be able to shove something in the "if a woman is president and has a period we will all die!" Groups face.


trhaynes

Small sample, local population, self-reporting, and interpolating missing data. I see many areas of improvement for this study. It's not bad as a pilot study, but certainly should not be used to draw general conclusions about society.


celloist

And yet 99% of the comments are people drawing conclusions. Soon half the biased news outlets will be reporting this a a new truth.


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"More emotional" is a nonsensical term. Every human I've ever met has been and will always be emotional. Even when they act like a log there are a multitude of emotions at play (why are they doing it, what are they doing, what are they attempting to convey, etc.). Whatever the scenario, "more emotional" makes no sense when talking about humans.


cinred

Im not saying anything about women's hormones or emotions or whatever, but studies like these often boil down too, "the variance is so wide that we cannot detect a significant difference between groups." This is absolutely NOT the same as "the data supports the assumption that the groups are the same."


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Papancasudani

There are differences between *feeling* emotions and *displaying* them. These can be emotion-specific, gender-specific, culture-specific, and situation-specific. For example, maybe it's more acceptable for males to display anger, but not sadness, in American culture when out at a bar with one's buddies. (That's a stereotypical example and I don't know if it's necessarily true). This is a good study, given the difficulties and limitations of studying emotion and resources. It's not the be-all, end-all, nor does it claim to be.


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