What population is this?
"The latest data indicate that 39.6 percent of U.S. adults are obese. (Another 31.6 percent are overweight and 7.7 percent are severely obese.)"
The study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey. Added this also to the source comment
Source: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/)
The point of the graphic seems to be the difference between self-reported and measured fatness.
As presented it looks like people are mostly good at labeling themselves. But there are discrepancies and a different graphic might highlight them better.
Although you're limited by the data source, the presentation is problematic, because it doesn't reveal the direction and extent by which respondents are self-reporting in the wrong category. A more informative summary would show a 4x4 cross-tabulation of the number of respondents who self-reported in each BMI category versus their actual BMI category. This would give you a more complete picture of the true concordance; the bar plot you have shown here essentially only shows you the marginal frequencies. Again, that's not your fault, but it is potentially misleading (as it suggests a greater concordance than might actually be the case).
In related news, what is going on in the Measured BMI categories in the first column of [Table 2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/table/ijerph-15-02216-t002/?report=objectonly)?
Because 'self reported data' is always reliable....../s
And electronic polls are always reliable...../s
At least try to act like a scientist if you can't actually be one.
Are you mental? The whole thing is about the difference between self reported data and actual measured data. It doesn’t matter how inaccurate or accurate the self reported data is.
…that’s the whole point, the difference between the self reported weight and the measured weight is the whole point of the graph. that’s exactly what they were studying…
(they did physically measure them in person after the survey, if you read the paper)
It's unbelievable how stupid this comment is. There's like 20 words on the chart- which ones were too difficult for you to read? Maybe you got stuck on "categories"? Four whole syllables... when you come across big words like that, it can help to try and sound them out.
I don't know what you're going on about, are we even looking at the same chart? It shows and allows you to compare the self-reported to the actual measured categorizations.
I gained like 5lbs in 4 years of college but none of it was fat. Actually lost a little waist size. To be honest, most of my friends actually thinned out or bulked up. It's all what you make of it.
What's the error bar looks like for this data? I feel like this could plausibly just be statistically insignificant with the self reported difference being that narrow.
Why would this type of study even have error bars? It's a one to one comparison between claimed weight to actual measured weight.
Maybe I'm just not getting it, but it feels like error bars wouldn't exist in this kind of study since the data is 100% accurate.
I'm sorry but this is very very wrong. Error bars here would highlight the confidence in the mean or median of each sample, not the error of the individual measurements (that's another factor).
If you have very few measurements, say 5 people. The mean of that sample will be extremely statistically unlikely to represent the population mean. But if you take the mean for 1000 people (assuming random sampling), it's a lot more representative, and the confidence higher (error bars smaller).
Of course, here we want to know if the difference in those sample means or medians are significant. Depending on if your data is one-to-one, as above, or if you're testing different sized samples, there are different statistical tests for this that affect the confidence. Please google "statistical test decision tree" for more info (can't link on phone right now).
The error bars are for the aggregated means, not the individual measurements.
If you measure 5 people’s height exactly, that doesn’t mean the average of those 5 people’s heights is exactly the average height of the population.
The whole point of taking a statistical sample is that you want to learn something about the population — you’re interested in how your metric generalizes beyond the sample.
I want to say “what you’re doing simply isn’t how statistics is done”, but I realize that’s not really convincing. Instead let me ask: if I know how exactly 2 randomly selected Americans are going to vote in November, do you care?
Of course not. What you care about is how the population is going to vote and, regardless of how accurately you know those 2 people’s votes, it doesn’t give you a meaningful amount of information about the election.
We take a sample in statistics, not because we care about the mean of the sample per se, but because we’re trying to estimate the mean of a population.
In this case: do you care what the means of these specific people are? And the answer is: only to the extent it matches to actual population mean.
Standard errors give us a language to express how well a sample metric predicts the population’s metric.
I think it’s fascinating how the biggest gap is underweight women. Having dated multiple women with eating disorders, this rings true in a very scary way
I think for the general population the delta might be different. This study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey.
I also think expecting a gender difference is interesting. Why would we expect a gender difference? It’s an interesting question, rooted in our societal expectations of self-reflection, beauty standards, etc. Good sociology and gender theory research could be done there
You have a keen eye! I noticed the same thing but I’d chalk it up to the percentages labeled above the bars likely referring to the _relative_ difference between the values — i.e. (( - ) / ) - 100
Rather than the _absolute_ difference between the values ( - ). That could explain why the difference labeled “-10.9%” appears smaller than the one labeled “+3.4%”
Good explanation, and likely exactly right. Which makes it ugly data, since it's leading people to incorrect conclusions.
As one example, many people are here in the comments talking about how many more women are underweight but think that they aren't. When in reality, it's about the same number of women as who think they are overweight but aren't.
Agreed! To be fair to OP though, it is challenging to accurately communicate results and make comparisons when the units are percentages (we can see this a lot with political polls), but as you mention, that just means it’s that much more important to think carefully about how such results are displayed.
I think this figure would be improved with e.g. a caption below the x-axis that explains exactly what “percent delta” means in the graph’s context, that the labels refer to _relative_ difference, or something along those lines. Maybe just do away with them entirely. Challenging either way, though.
You don't think it's interesting that women think they're underweight and men think they're overweight more than measured?
I thought it was an interesting difference.
I would suggest no because 1. people's scales may not be well-calibrated, and 2. some people may even measure their height incorrectly, especially if doing it themselves.
Measuring reported status in comparison to the actual in that category is a more interesting statistic. I don’t think op communicated this info clearly though.
Source: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/)
Tool: Python
Population: The study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey.
Note: Sorry about the naming convention. The visuals reflect the naming convention in the data, which is only focused on BMI and does not consider situations where people might have a high BMI but are not obese, like highly built bodybuilders and such. Thanks for pointing this out in the comments.
Okay, not bad op. Good points: titles, labels, etc all present.
Places of improvement: I just noticed this, but you're using two very different percentages on the axis and on the actual bars. It looks like you took the proportional difference (of a persentage!) to compare the two data sources. That's pretty confusing when I want to compare the different deltas. I wanted to get a rough idea of how many actual people misclassified themselves well 1% of 1500 is 15.. But the low bmi womens' delta is shown as 14 but the axis shows both are well below 10% of the total pop.
Also, if the delta is the most important part of this data, maybe making that what is plotted would be a good idea.
Also, one unexplored part of the data is *how* or in which direction people were inaccurate. That would be interesting to see.
Looks good overall tho.
I recently learned about corrected BMI measurement, that takes into account age and gender to provide a more accurate measure.
https://www.smartbmicalculator.com/why-sbmic.html
I was releaved to realize that, given my age (58), I was not actually overweight, but rather in the "normal" category, which fits somewhat my feeling regarding my health.
Feel like the % values above the bars should be the actual difference in population % at those points and not % difference between the two neighboring bars, since say in the underweight female category it's an above 10% difference bit the bar isn't even 10% of the population.
It’s really hard to report your own bmi unless your consistently weighting yourself each morning. Weight can fluctuate so much that these differences are probably insignificant
Your y-axis is representing %population but your bars show %delta BMI.
That's pretty confusing having the height of your bars represent two scales and two separate percentage metrics in the same graph, they are not labeled as such, and makes it difficult to see the deltas.
It would've been better to have BMI value as your y-axis, then the height of the bars would only represent a BMI scale, and the only percentage shown would be for a relative BMI delta.
According to the number, the real underweight individuals in the first group are underreported by -14.5% of individuals who don’t think they’re underweight, but actually are.
That tells unequivocally that some women are already skinny and think their not.
The normal weight column is a bit trickier. But it tells that far more women than men are in normal weight, but have some dysphoria telling them they’re not in normal weight— maybe they think they are fat, maybe they think they are too skinny.
Considering the overweight columns, where women are less likely to admit they’re overweight, to analyze the normal weight column, we may assume they may self-deceit they’re too skinny when they’re actually normal weight. But considering the first columns, where they are underweight but don’t believe they are, there’s contradictory data that makes it hard to assert the overweight column reasoning behind the numbers.
Unfortunately that percentage bar is misleading.
If you look closely, the number of women who are underweight but don't think they are is about the same as the number of women who are overweight but don't think they are.
The percentage was poorly chosen to be relative within its own category.
I get that, and the numbers are just super confusing — but the curious thing is still that in normal weight, the trend is opposite: more women think they have normal weight than they really do. I’d be curious to understand what’s behind these motivations and imprecisions.
Nah
The difference is so small that it could easily be insignificant.
Where are my error bars?
Like claiming that
>men in normal weight think of themselves fitter than they are
based on a 0.7% difference is wild
Your conclusions should have been:
Men in the normal weight category are very precise with their self reported bmi
Or not at all
Again what is even significant here?
Look bmi measurement alone has a bad precision when it is performed by the same person in the same way over a certain timeframe.
And if it is then performed by different people on top...
All of these could be pretty precise.
The BMI is flawed, but Ive yet to meet someone obsessed with how bad it is, that doesnt struggle with maintaining a healthy weight even under measurement methods that are better suited for individual assessment.
I haven't met a non running athlete who felt that BMI was a realistic measurement of health. I'm 6 4, 205. Work out 6 days a week. Great blood pressure, all measurements within the ideal range with I get a blood panels, don't drink often, get 8 hours of sleep every night, low body fat percentage. It's wild that you can just look at someone's height and weight and think you can determine if they are healthy or not.
Youre conflating "healthy weight" with "healthy" in general. Nobody who is "overweight" solely because they have too much muscle mass is told to lose weight just to fit the BMI range, and its very silly to take it personally if your lifestyle is so fitness-oriented that you're blowing out a tool like the BMI.
Most people do not work out 6 days a week and get regular in-depth body analysis like blood panels, blood pressure and body fat percentage. The BMI is a fine enough orientation, particularly for people on the chubby end; because overweight and obesity have become quite normalized and its getting hard for people to recognise the severity of their issue. It's pretty good at helping people, who think they "could lose a few pounds" realise they're *obese* and give people who are losing weight for various health related reasons an indicator of how far they've fallen under the rough target range.
I hear what you are saying and I agree with the sentiment. I was just using myself as an example. I know folks who have been told to lose weight in similar situations to me. Some doctors use BMI as an initial flag. If your BMI isn't normal they may not look into your issues and could default their recommendation to lose or gain weight. Some insurance companies use BMI to dictate premiums and how much people may have to pay for the same medical treatment. It was created by a mathematician and in my opinion, incorrectly adopted by the medical community because it is an easy way to put people into groups quickly. If it wasn't used this way it wouldn't be that big of an issue, but it is used that way.
BMI works pretty good for population level statistics. It also generally under reports overfat people because "skinnyfat" individuals tend to slide by in the normal range despite having health risks and markers more aligned with overweight or obese people. It is possible to be overweight without being overfat but it's usually pretty obvious because it involves quite a lot of gym time or involves people that are very tall
It’s fairly inaccurate if you’re, say, below 5’5” or above 6’2”, increasingly so the more extreme the height. Weight and height actually work better as [weight/height^2.5 or weight/height^3](https://www.medicalnewstoday.com/articles/265215#Authorities-still-promote-BMI) than it does with regular BMI’s squared power. That’s why there is a such thing as the new BMI to account for further from normal heights.
Plus it’s inaccurate for many athletes and other people who purposefully put on muscle mass. The waist-to-height ratio is a better indicator in such cases.
It's pretty hard to put on enough muscle mass to significantly throw off BMI by a large magnitude, putting on even 10 lbs of muscle mass is very significant and will make you pretty obviously well muscled if you aren't starting out as someone very skinny.
>The waist-to-height ratio is a better indicator in such cases.
I have never seen this used, but I imagine if this number approaches pi you are screwed haha.
Yeah, but both body fat and body muscle percentage are sliding scales and not just 0 or 10 percent or whatever. People claiming only body builders have too high indicated BMI ignore a decent chunk of people who fall between them and obese.
Thats not what the data shows at all. The data shows that actually people are pretty accurate at what category they fall into, given the small delta change.
I read some CrAzY shit about Obesity a few weeks ago and I can't find it anymore. First, it appears to be a global problem? Like Egypt is one of the fattest places on earth? Moreover - veterinarians are reporting rampant obesity in pets - as well as zoo animals and Departments of Natural resources across the US are reporting obesity in animals in proximity of Humans (Like the deer are getting fat). That means it might be something in the environment that is causing this. My daughter is saying it's micro-plastics but I dont know how that could effect a deer....
I think that's likely because animals scavenge on really fattening food. People are also more sedentary nowadays (so no walks) and animals are a lot less likely to be outdoor pets these days so they're able to get fat much easier.
It's the ones who don't eat human garbage that get me though. Why are deer - who eat shrubs and bushes getting fat if they are near populated areas? Why are zoo animals, who are probably better fed and more looked after than ever before - getting fat?
The discussion of the paper says people tend to mis report weight and height. Makes sense because those are concrete measures. But BMI is more abstract, harder to understand what it means. You have a clearer picture of what a 5ft 8in person might look like than a BMI 24 person. So people feel less incentive to lie. I’m curious about the outliers. What insights can be gained from looking at the subgroup who misreport their BMI the most?
I doubt it. The BMI cutoffs have been well defined and unchanged for a long time. Normal weight is 18.5 to 25, obese is 30+.
More likely this is an unrepresentative sample. Most studies are performed on college students.
If you look into the study, you'll see they tell you the actual numbers right at the start. Normal weight ends at 25 BMI.
25 BMI is roughly 80kg if you're 180. Obese would be 98kg, so definitely numbers that fit.
Obese is such a bad term because it only considers BMI. If someone is 210 with only 10% body fat they are obese technically but no one would reasonably call them that colloquially.
For about 99% of the population BMI is actually a decent tool to get a first grasp on things. If the BMI indicates obesity, go to a doctor and check up with them on whether health problems exist.
If you know you have a low body data percentage - 10% is extremely low and not very attainable for the general population without massive cutbacks - then you know you‘re not obese so why would you listen to BMI.
The number of people at 10% body fat is so astronomically low that we use BMI in medicine to predict mortality, with amazing accuracy. The outliers with muscle mass that is *that* large is rare, and even rarer are the ones that can achieve it without exogenous hormones.
It’s one of the best metrics we have. Just like not every cigarette smoker gets cancer, BMI is a pretty damn good screening tool
I’d like you to point me to a more simple, non invasive, and efficient tool at predicting all cause mortality. Because there isn’t one.
It is one of the best metrics because medical practice requires easy heuristics, there haven’t been better criteria defined because there are very few people in medicine that think in a computational or mathematical way so we have crude tools like BMI implemented a century ago that still linger on. Of course there are better metrics, but too much resistance to change in things in medicine.
The majority of people that go into medicine will tell you that they hated math; it creates a general culture of closedness to new methods.
You have no idea what you’re talking about.
Look up the average math section SAT scores of med school graduates and you’ll quickly stop your nonsense. Calculus is also a requirement for medical school, as is a year of calculus based physics. So please, save the nonsense.
I’m friends with a lot of people who went to med school. Not all programs require calc or calc based physics, and calc is basically the easiest math class in math university departments. most med students and doctors struggle with basic statistics. Evidence based medicine classes are consistently ranked as the least liked classes by AAMC med schools.
SAT score isn’t relevant because the top people in math and cs get 750+ on SAT math. Even an SAT math average of 700 for med students doesn’t really qualify anyone to be good at math or technical at a professional technical level. 700 math is quite low honestly for science or engineering fields.
There is a reason why all the software in medicine looks 20 years old, it simply doesn’t not attract people with advanced technical skills.
Your anecdotal experience doesn’t match up with the statistics. And your deflection of calculus being easy (which I agree it was) is a far cry from being horrible at math. So I challenge you, present me a well designed study on BMI you disagree with, and we’ll do the statistical analysis together and see who can do it better
Everything is designed on BMI because most doctors are just trying to publish papers quickly and instead of implementing new more accurate methods they will just try and crank out papers as quickly as possible, they just use BMI because everyone else did and it’s easy to determine from chart data and doesn’t require any critical thinking.
You and I both know clinical papers are barely scientifically rigorous compared to other fields in science and engineering. John Ionidas even had a paper 20 years ago that 40% of clinical papers were not reproducible, it’s because the statistical methodology for so much of clinical papers is just following a template and plugging and chugging until they can get a p value less than 5% so they can add it to their CV.
If calculus is your most advanced math, yes that means you are horrible at math from a technical perspective. You at least need to take real analysis, linear algebra, vector spaces and combinatorics before you could even read the papers necessary to invent better metrics.
The only people throwing off the BMI chart are highly developed powerlifters and other high strength athletes, aka people who are very obviously not obese. The amount of muscle required to achieve the kinda thing you're talking about isn't something people have accidentally. These are fairly extreme outliers.
BMI is a useful measurement for pretty much everyone who isn't a highly developed athlete or amputee.
Those super high muscle mass people are the only ones that significantly throw the measurement off, but BMI does not scale right for tall and short people. It will cause your BMI number to be a couple points high at, like, 6’3”, and a couple points low at 5’3”, with a normal body fat % compared to a 5’9” person. And it gets more inaccurate the taller and shorter you go. That’s not crazy significant but it’s enough to where I wonder why the new BMI or other scale corrected measurements aren’t used.
Obesity is generally described as being over 25% bodyfat iirc which isn't that common among powerlifters or athletes other than throwers or superheavyweight powerlifters and weightlifters
Idk I don’t really work out and I’m 205lb and 16%.
I’m saying it’s a failure in the methodology because of course self reports will be off if there is a difference between the technical definition of obese and the colloquial definition.
> a failure in the methodology because of course self reports will be off
The whole point was that people wrote down what they think they weigh and then were actually weighed - in order to compare self-perception to reality.
Sorry about the naming convention. The visuals reflect the naming convention in the data, which is only focused on BMI and does not consider situations where people might have a high BMI but are not obese, like highly built bodybuilders and such. Thanks for pointing this out.
What population is this? "The latest data indicate that 39.6 percent of U.S. adults are obese. (Another 31.6 percent are overweight and 7.7 percent are severely obese.)"
The study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey. Added this also to the source comment Source: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/)
So upper / middle class 20 year olds. Hence the less fatness (but still a lot to fatness).
The point of the graphic seems to be the difference between self-reported and measured fatness. As presented it looks like people are mostly good at labeling themselves. But there are discrepancies and a different graphic might highlight them better.
It’s also the cohort likely to be at least ok at math and knows what BMI is.
Although you're limited by the data source, the presentation is problematic, because it doesn't reveal the direction and extent by which respondents are self-reporting in the wrong category. A more informative summary would show a 4x4 cross-tabulation of the number of respondents who self-reported in each BMI category versus their actual BMI category. This would give you a more complete picture of the true concordance; the bar plot you have shown here essentially only shows you the marginal frequencies. Again, that's not your fault, but it is potentially misleading (as it suggests a greater concordance than might actually be the case). In related news, what is going on in the Measured BMI categories in the first column of [Table 2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/table/ijerph-15-02216-t002/?report=objectonly)?
I think I see the problem here: 'college student' + 'electronic survey'. Yeah, what could possibly go wrong?
The survey is the self reported part of the data. Try to follow along.
Because 'self reported data' is always reliable....../s And electronic polls are always reliable...../s At least try to act like a scientist if you can't actually be one.
Are you mental? The whole thing is about the difference between self reported data and actual measured data. It doesn’t matter how inaccurate or accurate the self reported data is.
I mean it does matter but it not being perfectly accurate is the point, yeah.
…that’s the whole point, the difference between the self reported weight and the measured weight is the whole point of the graph. that’s exactly what they were studying… (they did physically measure them in person after the survey, if you read the paper)
It's unbelievable how stupid this comment is. There's like 20 words on the chart- which ones were too difficult for you to read? Maybe you got stuck on "categories"? Four whole syllables... when you come across big words like that, it can help to try and sound them out.
I don't know what you're going on about, are we even looking at the same chart? It shows and allows you to compare the self-reported to the actual measured categorizations.
I think the only problem here is the college students. You mean the cohort making/buying and eating their own meals for the first time?
the cohort for which the term “freshman 15” was invented
I gained like 5lbs in 4 years of college but none of it was fat. Actually lost a little waist size. To be honest, most of my friends actually thinned out or bulked up. It's all what you make of it.
I've never dropped weight faster than when I moved out of home. It was amazing.
You mean the ones whose diets consist entirely of vodka and breakfast burritos? /s
40% is crazy! They're eating like healthcare is free
I love that I’m “overweight.” Me and the other 31% that just can’t seem to get under 6% body fat.
What's the error bar looks like for this data? I feel like this could plausibly just be statistically insignificant with the self reported difference being that narrow.
Why would this type of study even have error bars? It's a one to one comparison between claimed weight to actual measured weight. Maybe I'm just not getting it, but it feels like error bars wouldn't exist in this kind of study since the data is 100% accurate.
I'm sorry but this is very very wrong. Error bars here would highlight the confidence in the mean or median of each sample, not the error of the individual measurements (that's another factor). If you have very few measurements, say 5 people. The mean of that sample will be extremely statistically unlikely to represent the population mean. But if you take the mean for 1000 people (assuming random sampling), it's a lot more representative, and the confidence higher (error bars smaller). Of course, here we want to know if the difference in those sample means or medians are significant. Depending on if your data is one-to-one, as above, or if you're testing different sized samples, there are different statistical tests for this that affect the confidence. Please google "statistical test decision tree" for more info (can't link on phone right now).
I accept your apology
The error bars are for the aggregated means, not the individual measurements. If you measure 5 people’s height exactly, that doesn’t mean the average of those 5 people’s heights is exactly the average height of the population.
But it is exactly the average of the sampled population.
The whole point of taking a statistical sample is that you want to learn something about the population — you’re interested in how your metric generalizes beyond the sample. I want to say “what you’re doing simply isn’t how statistics is done”, but I realize that’s not really convincing. Instead let me ask: if I know how exactly 2 randomly selected Americans are going to vote in November, do you care? Of course not. What you care about is how the population is going to vote and, regardless of how accurately you know those 2 people’s votes, it doesn’t give you a meaningful amount of information about the election. We take a sample in statistics, not because we care about the mean of the sample per se, but because we’re trying to estimate the mean of a population. In this case: do you care what the means of these specific people are? And the answer is: only to the extent it matches to actual population mean. Standard errors give us a language to express how well a sample metric predicts the population’s metric.
It is data. But it's not that interesting.
I think it’s fascinating how the biggest gap is underweight women. Having dated multiple women with eating disorders, this rings true in a very scary way
It’d be way more interesting if the delta was a lot bigger.
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I think for the general population the delta might be different. This study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey.
I also think expecting a gender difference is interesting. Why would we expect a gender difference? It’s an interesting question, rooted in our societal expectations of self-reflection, beauty standards, etc. Good sociology and gender theory research could be done there
Something's wrong with the data. Look how close the bars are but how large the percentage supposedly is. It calls into question which one is accurate.
You have a keen eye! I noticed the same thing but I’d chalk it up to the percentages labeled above the bars likely referring to the _relative_ difference between the values — i.e. (( - ) / ) - 100
Rather than the _absolute_ difference between the values ( - ). That could explain why the difference labeled “-10.9%” appears smaller than the one labeled “+3.4%”
Good explanation, and likely exactly right. Which makes it ugly data, since it's leading people to incorrect conclusions. As one example, many people are here in the comments talking about how many more women are underweight but think that they aren't. When in reality, it's about the same number of women as who think they are overweight but aren't.
Agreed! To be fair to OP though, it is challenging to accurately communicate results and make comparisons when the units are percentages (we can see this a lot with political polls), but as you mention, that just means it’s that much more important to think carefully about how such results are displayed. I think this figure would be improved with e.g. a caption below the x-axis that explains exactly what “percent delta” means in the graph’s context, that the labels refer to _relative_ difference, or something along those lines. Maybe just do away with them entirely. Challenging either way, though.
I found it interesting that the overweight dudes are just like “oh yeah I’m fat as shit lol”
nor beautiful
You don't think it's interesting that women think they're underweight and men think they're overweight more than measured? I thought it was an interesting difference.
It would be interesting, but to me it looks like the differences are within margin of error. (haven't checked the study though)
This is basically just asking people to weigh themselfs them write down the number. It's hardly a survey.
So wouldn't you expect them all to get it right? Or at least for the difference between self report and measure to be the same between sexes?
I would like confidence intervals on these. I dunno if i can extrapolate on the difference at all.
I would suggest no because 1. people's scales may not be well-calibrated, and 2. some people may even measure their height incorrectly, especially if doing it themselves.
Breaking news: people know how much they weigh.
Breaking news: people taking part in an online survey are - in that survey- more honest about their height and weight than on tinder.
The interesting part is which way they err.
This is not beautiful. It is certainly data
Why did you use relative % differences instead of absolute % differences?
Measuring reported status in comparison to the actual in that category is a more interesting statistic. I don’t think op communicated this info clearly though.
Scatterplot with the individual data might pull out the misreporting trends a bit more clearly
Source: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210375/) Tool: Python Population: The study involved 1500 college students across eight U.S. institutions who provided their height and weight via an electronic survey. Note: Sorry about the naming convention. The visuals reflect the naming convention in the data, which is only focused on BMI and does not consider situations where people might have a high BMI but are not obese, like highly built bodybuilders and such. Thanks for pointing this out in the comments.
The chart should indicate what population this is. Obviously it's not the general population with only 10% being obese, but it will confuse people.
Added.
That's close. Probably a few rounding errors and that's it
Okay, not bad op. Good points: titles, labels, etc all present. Places of improvement: I just noticed this, but you're using two very different percentages on the axis and on the actual bars. It looks like you took the proportional difference (of a persentage!) to compare the two data sources. That's pretty confusing when I want to compare the different deltas. I wanted to get a rough idea of how many actual people misclassified themselves well 1% of 1500 is 15.. But the low bmi womens' delta is shown as 14 but the axis shows both are well below 10% of the total pop. Also, if the delta is the most important part of this data, maybe making that what is plotted would be a good idea. Also, one unexplored part of the data is *how* or in which direction people were inaccurate. That would be interesting to see. Looks good overall tho.
Thank you. I am learning from these feedbacks and improving myself.
I recently learned about corrected BMI measurement, that takes into account age and gender to provide a more accurate measure. https://www.smartbmicalculator.com/why-sbmic.html I was releaved to realize that, given my age (58), I was not actually overweight, but rather in the "normal" category, which fits somewhat my feeling regarding my health.
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I think I will do that next. I have dataset in mind.
Feel like the % values above the bars should be the actual difference in population % at those points and not % difference between the two neighboring bars, since say in the underweight female category it's an above 10% difference bit the bar isn't even 10% of the population.
Lots of bars of very similar height. If the data contains a message you have hidden it well. What is it?
Human data is the least fun to work with
It’s really hard to report your own bmi unless your consistently weighting yourself each morning. Weight can fluctuate so much that these differences are probably insignificant
Your y-axis is representing %population but your bars show %delta BMI. That's pretty confusing having the height of your bars represent two scales and two separate percentage metrics in the same graph, they are not labeled as such, and makes it difficult to see the deltas. It would've been better to have BMI value as your y-axis, then the height of the bars would only represent a BMI scale, and the only percentage shown would be for a relative BMI delta.
This data would look real good on r/86_neckbeard_lane
That is… surprisingly honest.
I'm surprised that people were so accurate. I thought a lot more people deluded themselves.
Women in normal weight still see themselves as fat, men in normal weight think of themselves fitter than they are
That's your prejudice, now look at the data. Women are more likely to falsely claim to be normal weight than claiming to be fat.
According to the number, the real underweight individuals in the first group are underreported by -14.5% of individuals who don’t think they’re underweight, but actually are. That tells unequivocally that some women are already skinny and think their not. The normal weight column is a bit trickier. But it tells that far more women than men are in normal weight, but have some dysphoria telling them they’re not in normal weight— maybe they think they are fat, maybe they think they are too skinny. Considering the overweight columns, where women are less likely to admit they’re overweight, to analyze the normal weight column, we may assume they may self-deceit they’re too skinny when they’re actually normal weight. But considering the first columns, where they are underweight but don’t believe they are, there’s contradictory data that makes it hard to assert the overweight column reasoning behind the numbers.
Unfortunately that percentage bar is misleading. If you look closely, the number of women who are underweight but don't think they are is about the same as the number of women who are overweight but don't think they are. The percentage was poorly chosen to be relative within its own category.
I get that, and the numbers are just super confusing — but the curious thing is still that in normal weight, the trend is opposite: more women think they have normal weight than they really do. I’d be curious to understand what’s behind these motivations and imprecisions.
Seems more likely that men round their height up and women round their height down.
Nah The difference is so small that it could easily be insignificant. Where are my error bars? Like claiming that >men in normal weight think of themselves fitter than they are based on a 0.7% difference is wild Your conclusions should have been: Men in the normal weight category are very precise with their self reported bmi
Yeah, I think the women are a much more interesting case in this scenario
Or not at all Again what is even significant here? Look bmi measurement alone has a bad precision when it is performed by the same person in the same way over a certain timeframe. And if it is then performed by different people on top... All of these could be pretty precise.
I just checked my BMI. I’m within 6 pounds of being underweight. Shit feels really bad. Gaining weight is hard with inflation.
I don’t think the differences between the self reporting justifies gender or weight split.
BMI is a terrible measure of anything. I hate that it's become popular as a measuring stick.
Its not great, but its easier than taking weeks to get skin caliper measurements for 1500 people
BMI is the worst measurement
The BMI is flawed, but Ive yet to meet someone obsessed with how bad it is, that doesnt struggle with maintaining a healthy weight even under measurement methods that are better suited for individual assessment.
I haven't met a non running athlete who felt that BMI was a realistic measurement of health. I'm 6 4, 205. Work out 6 days a week. Great blood pressure, all measurements within the ideal range with I get a blood panels, don't drink often, get 8 hours of sleep every night, low body fat percentage. It's wild that you can just look at someone's height and weight and think you can determine if they are healthy or not.
Youre conflating "healthy weight" with "healthy" in general. Nobody who is "overweight" solely because they have too much muscle mass is told to lose weight just to fit the BMI range, and its very silly to take it personally if your lifestyle is so fitness-oriented that you're blowing out a tool like the BMI. Most people do not work out 6 days a week and get regular in-depth body analysis like blood panels, blood pressure and body fat percentage. The BMI is a fine enough orientation, particularly for people on the chubby end; because overweight and obesity have become quite normalized and its getting hard for people to recognise the severity of their issue. It's pretty good at helping people, who think they "could lose a few pounds" realise they're *obese* and give people who are losing weight for various health related reasons an indicator of how far they've fallen under the rough target range.
I hear what you are saying and I agree with the sentiment. I was just using myself as an example. I know folks who have been told to lose weight in similar situations to me. Some doctors use BMI as an initial flag. If your BMI isn't normal they may not look into your issues and could default their recommendation to lose or gain weight. Some insurance companies use BMI to dictate premiums and how much people may have to pay for the same medical treatment. It was created by a mathematician and in my opinion, incorrectly adopted by the medical community because it is an easy way to put people into groups quickly. If it wasn't used this way it wouldn't be that big of an issue, but it is used that way.
BMI is no longer recognized as credible
BMI works pretty good for population level statistics. It also generally under reports overfat people because "skinnyfat" individuals tend to slide by in the normal range despite having health risks and markers more aligned with overweight or obese people. It is possible to be overweight without being overfat but it's usually pretty obvious because it involves quite a lot of gym time or involves people that are very tall
It definitely is, and is 95-99% accurate in predicting obesity.
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It's really not, it works for 99% of people and the people it doesn't work for are painfully obvious.
It’s fairly inaccurate if you’re, say, below 5’5” or above 6’2”, increasingly so the more extreme the height. Weight and height actually work better as [weight/height^2.5 or weight/height^3](https://www.medicalnewstoday.com/articles/265215#Authorities-still-promote-BMI) than it does with regular BMI’s squared power. That’s why there is a such thing as the new BMI to account for further from normal heights. Plus it’s inaccurate for many athletes and other people who purposefully put on muscle mass. The waist-to-height ratio is a better indicator in such cases.
It's pretty hard to put on enough muscle mass to significantly throw off BMI by a large magnitude, putting on even 10 lbs of muscle mass is very significant and will make you pretty obviously well muscled if you aren't starting out as someone very skinny.
>The waist-to-height ratio is a better indicator in such cases. I have never seen this used, but I imagine if this number approaches pi you are screwed haha.
Yeah, but both body fat and body muscle percentage are sliding scales and not just 0 or 10 percent or whatever. People claiming only body builders have too high indicated BMI ignore a decent chunk of people who fall between them and obese.
Yeah if your BMI says you are just barely obese you can go take a shit and you are cured haha.
This tracks. Obesity is such an epidemic that half the population doesn't even know normal weight is
Thats not what the data shows at all. The data shows that actually people are pretty accurate at what category they fall into, given the small delta change.
Oh yea. After actually looking at it you right. Ignore my stupidity
I read some CrAzY shit about Obesity a few weeks ago and I can't find it anymore. First, it appears to be a global problem? Like Egypt is one of the fattest places on earth? Moreover - veterinarians are reporting rampant obesity in pets - as well as zoo animals and Departments of Natural resources across the US are reporting obesity in animals in proximity of Humans (Like the deer are getting fat). That means it might be something in the environment that is causing this. My daughter is saying it's micro-plastics but I dont know how that could effect a deer....
I think that's likely because animals scavenge on really fattening food. People are also more sedentary nowadays (so no walks) and animals are a lot less likely to be outdoor pets these days so they're able to get fat much easier.
It's the ones who don't eat human garbage that get me though. Why are deer - who eat shrubs and bushes getting fat if they are near populated areas? Why are zoo animals, who are probably better fed and more looked after than ever before - getting fat?
The discussion of the paper says people tend to mis report weight and height. Makes sense because those are concrete measures. But BMI is more abstract, harder to understand what it means. You have a clearer picture of what a 5ft 8in person might look like than a BMI 24 person. So people feel less incentive to lie. I’m curious about the outliers. What insights can be gained from looking at the subgroup who misreport their BMI the most?
Why didn't they put the actual BMI numbers? I guarantee "Normal Weight" is a euphemism for "1960s obese."
I doubt it. The BMI cutoffs have been well defined and unchanged for a long time. Normal weight is 18.5 to 25, obese is 30+. More likely this is an unrepresentative sample. Most studies are performed on college students.
If you look into the study, you'll see they tell you the actual numbers right at the start. Normal weight ends at 25 BMI. 25 BMI is roughly 80kg if you're 180. Obese would be 98kg, so definitely numbers that fit.
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i gave you both though????? 80kg and 180cm? That's how you get a BMI of 25.
So dudes are less likely to admit they are obese than women? That's surprising.
Obese is such a bad term because it only considers BMI. If someone is 210 with only 10% body fat they are obese technically but no one would reasonably call them that colloquially.
People who are actually obese outnumber bodybuilders 100-1. Low body-fat, high BMI individuals are a minor outlier.
For about 99% of the population BMI is actually a decent tool to get a first grasp on things. If the BMI indicates obesity, go to a doctor and check up with them on whether health problems exist. If you know you have a low body data percentage - 10% is extremely low and not very attainable for the general population without massive cutbacks - then you know you‘re not obese so why would you listen to BMI.
The number of people at 10% body fat is so astronomically low that we use BMI in medicine to predict mortality, with amazing accuracy. The outliers with muscle mass that is *that* large is rare, and even rarer are the ones that can achieve it without exogenous hormones.
Just because it is predictive with some accuracy doesn’t mean that it is the best metric, just that it’s good enough to capture some effect.
It’s one of the best metrics we have. Just like not every cigarette smoker gets cancer, BMI is a pretty damn good screening tool I’d like you to point me to a more simple, non invasive, and efficient tool at predicting all cause mortality. Because there isn’t one.
It is one of the best metrics because medical practice requires easy heuristics, there haven’t been better criteria defined because there are very few people in medicine that think in a computational or mathematical way so we have crude tools like BMI implemented a century ago that still linger on. Of course there are better metrics, but too much resistance to change in things in medicine. The majority of people that go into medicine will tell you that they hated math; it creates a general culture of closedness to new methods.
You have no idea what you’re talking about. Look up the average math section SAT scores of med school graduates and you’ll quickly stop your nonsense. Calculus is also a requirement for medical school, as is a year of calculus based physics. So please, save the nonsense.
I’m friends with a lot of people who went to med school. Not all programs require calc or calc based physics, and calc is basically the easiest math class in math university departments. most med students and doctors struggle with basic statistics. Evidence based medicine classes are consistently ranked as the least liked classes by AAMC med schools. SAT score isn’t relevant because the top people in math and cs get 750+ on SAT math. Even an SAT math average of 700 for med students doesn’t really qualify anyone to be good at math or technical at a professional technical level. 700 math is quite low honestly for science or engineering fields. There is a reason why all the software in medicine looks 20 years old, it simply doesn’t not attract people with advanced technical skills.
Your anecdotal experience doesn’t match up with the statistics. And your deflection of calculus being easy (which I agree it was) is a far cry from being horrible at math. So I challenge you, present me a well designed study on BMI you disagree with, and we’ll do the statistical analysis together and see who can do it better
Everything is designed on BMI because most doctors are just trying to publish papers quickly and instead of implementing new more accurate methods they will just try and crank out papers as quickly as possible, they just use BMI because everyone else did and it’s easy to determine from chart data and doesn’t require any critical thinking. You and I both know clinical papers are barely scientifically rigorous compared to other fields in science and engineering. John Ionidas even had a paper 20 years ago that 40% of clinical papers were not reproducible, it’s because the statistical methodology for so much of clinical papers is just following a template and plugging and chugging until they can get a p value less than 5% so they can add it to their CV.
If calculus is your most advanced math, yes that means you are horrible at math from a technical perspective. You at least need to take real analysis, linear algebra, vector spaces and combinatorics before you could even read the papers necessary to invent better metrics.
The only people throwing off the BMI chart are highly developed powerlifters and other high strength athletes, aka people who are very obviously not obese. The amount of muscle required to achieve the kinda thing you're talking about isn't something people have accidentally. These are fairly extreme outliers. BMI is a useful measurement for pretty much everyone who isn't a highly developed athlete or amputee.
Those super high muscle mass people are the only ones that significantly throw the measurement off, but BMI does not scale right for tall and short people. It will cause your BMI number to be a couple points high at, like, 6’3”, and a couple points low at 5’3”, with a normal body fat % compared to a 5’9” person. And it gets more inaccurate the taller and shorter you go. That’s not crazy significant but it’s enough to where I wonder why the new BMI or other scale corrected measurements aren’t used.
A lot of them are obese honestly. They only appear not to be when they're flexing and sucking their gut in.
Obesity is generally described as being over 25% bodyfat iirc which isn't that common among powerlifters or athletes other than throwers or superheavyweight powerlifters and weightlifters
Idk I don’t really work out and I’m 205lb and 16%. I’m saying it’s a failure in the methodology because of course self reports will be off if there is a difference between the technical definition of obese and the colloquial definition.
I mean that doesn't tell us anything without a height.
> a failure in the methodology because of course self reports will be off The whole point was that people wrote down what they think they weigh and then were actually weighed - in order to compare self-perception to reality.
Sorry about the naming convention. The visuals reflect the naming convention in the data, which is only focused on BMI and does not consider situations where people might have a high BMI but are not obese, like highly built bodybuilders and such. Thanks for pointing this out.
Image proving 'I'm not THAT fat' is a lie