The key claim: "Random data actually mimics the effect really well."
This makes some sense. If people are asked to guess a number between 1 and 6 and then roll a die, the people who roll low are more likely to overestimate and the people who roll high are more likely to underestimate. But the key is precisely how well random data mimics the effect.
They're simulating randomness incorrectly: relationship between true and perceived will average 0.5, not 0; and bias will average 50%, not 0%. That's why their "random data" is sloped.
Add negative relationship and negative bias, and the random data will act as intended - hovering randomly around 50%.
Indeed. And the obvious reason is that when you simulate the "self-assessment" using a Gaussian noise around the "actual intelligence", and clamp it to [0, 100] so that it doesn't go "out of bound" (eg "negative intelligence" is not allowed), you will necessarily skew the low scores upward and the high scores downwards.
But it's not because "some statistical model exhibit a bias that's similar to the result" that this implies "therefore the result is a statistical error"... that's a backward reasonning
It’s obviously real, at least as used in conversation, whether it meets some rigorous definition I’m sure there’s an out, but we’ve all known these people. With vibe coding they’re everywhere. Is this going to be a modern “begging the question” where everyone knows what you mean but someone pipes up that actually the technical meaning is different?
The problem is that the original formulation was "most people" are unaware of being unskilled, but by now the name is used to mean "some group of people" is unaware of being unskilled. In that sense the article confirms that “a small number are", 5-6%, and therefore the effect exists in the colloquial sense but not in the scientific sense.
Mentioning the Dunning-Krueger effect incorrectly is wonderfully meta...
My experience with new CS grads was that most of them greatly overestimated what they knew, or alternatively, underestimated how much they did not know.
My experience with every person was that most of them overestimate what they know regardless of experience level. You just notice that more in new grads since its easier to tell when people are wrong about simple things than when they are wrong about more difficult things.
Software engineers tend to repeat the mantra "you cannot make accurate time estimates". That is true regardless of experience level, and everyone seems to be off by about the same amount. So there we have evidence that people overestimate their skills at every level, and its not that different.
Which is exactly what the Dunner-Kruger effect is. Knowledgeable people statistically underestimating their knowledge and non-knowledgeable people overestimating their knowledge.
I think the third one is a separate thing. I don’t know of a name for it, but I’d call it the engineer’s (or physicist’s) blindspot or something like that.
I can’t remember of DK suggests some sort of effect where the expert has undue self-doubt, though…
There's also the idea on the other side of the coin - whereby experts on a topic underestimate how much they know about it compared to the average person.
Even if it isn’t true, it’s got the feeling of truthiness (1).
I don’t expect it to ever go out of the public consciousness. Like other things that were never real like Stockholm Syndrome I suspect it’s just stuck in the zeitgeist now.
Power poses, imposter syndrome, Stanford prison experiment, marshmallow test...
It seems like the overlap between "real psychological effect" and "subtle enough that it requires research to discover" is vanishingly small. I guess that's not really surprising.
Very hard to understand the meat behind all the fluff of the article, especially as the simulation code is not available, and as the presented simulated and original graphs are effectively the same (I don't see a disagreement).
It's clear that the perceived curve will be differently sloped, as no one will evaluate themselves as the topmost or the bottommost percentiles, so the edges will be biased.
And if in both cases we draw differences between perceived and actual, we will get the same curve that everyone knows, biased or not.
It's annoying they didn't plot the new and old "perceived" curves on the same figure, but if you pay attention to the y-axis, there is a very big difference.
In the old plot, the bottom quartile has about a 50 percentage point margin between actual and perceived performance while the new one is 30 percentage points, which is a 50% difference between the old and new curve. The second quartile has 3x more margin in the old version relative to the new one.
So what does that change? If there's no error bars on the graphs you can't discuss significant differences easily. And if they do differ, plotting differences will yield the U shape curve.
Now it's much more clear. The simulated data tries generating the true relationship between actual and perceived scores from 0.0 to 1.0, and bias in self-reporting from 0% to 100%.
So the output graph should be the average of all these data generating processes, yielding perceived relationship around 0.5 and bias around 50%, with some high variation.
If you have the access, run their Shiny code with these values, and you will see the published plot.
I'd argue that this demonstration is much weaker than "making original no more meaningful than random". It's more that the "simulated 50% bias and 0.5 true correlation looks similar to what DK published", which is also far fetched given the data generation they did.
Note: true random (what they were going for) would cover negative relationships, yielding the random true relationship around 0; and if they wouldn't correct the sign of Bias, it would also average at around 0; yielding a realistic "random" with the slope hovering about 50% for any percentile.
I wouldn’t assume the cause of the replication crisis is 100% due to a lack of rigor in psychology and the other relevant fields. These fields deal with concepts and phenomena that are often abstract and difficult to measure. In some sense, it’s just harder.
Besides, deciding what is and isn’t science is a question for the philosophy of science, not science itself.
Hard in "hard science" doesn't mean hard as in difficulty, it means hard as in not soft. Soft sciences are difficult to explore since they aren't rigid, they move around as you prod at them etc, you can't get a good grasp of its shape since they are so soft.
Physics on the other hand is hard as in unyielding. It is easy to figure out boundaries of physics and map out what is and isn't true, and the few cases were we made a mistake everyone can agree a mistake was made and that formulas needs to be updated since physics is so extremely hard that even a tiny error will get noticed.
Hope that clears it up, physics isn't hard as in difficult, its hard as in rigid. And psychology is soft, not easy.
So, your statement doesn't make sense at all in this discussion, they just said psychology has soft traits, and then you say "ok, so its hard since its soft!". No, soft is difficult, not hard.
So for example, physics is like describing the shape of a metal spoon, and psychology is like describing the shape of a pillow. You can see how describing the shape of the pillow is massively more difficult, because its not fixed, so you have to come up with a language to describe all the ways it can deform and how that would work.
If you ever took psych 101, it was immediately obvious it's mostly horse shit. Made up nonsense theory, with studies that have few participants and are done by people who don't know math.
The strict academic definition hasn’t followed the colloquial usage for a long time. Maybe ever:
If a specific novice is over-confident and out of their depth, we say “Dunning-Kruger”
If a specific is under-confident and performing better than their self-estimate, that’s not commonly considered Dunning Kruger, in the colloquial use. It’s called imposter syndrome, or not labeled at all.
The researchers aren’t really disagreeing with that. They found that novices had a wider range of self-estimates of their performance than experienced people. So in the novice group you were more likely to find someone who was grossly over-confident in their abilities, but you also found people who underestimated themselves.
> instead showed that both experts and novices underestimate and overestimate their skills with the same frequency. “It’s just that experts do that over a narrower range,” he wrote to me.
Which doesn’t precisely contradict the idea that among novices you can find people who overestimate their skills. Which is how it’s commonly used.
So I can believe it’s a statistical wash when averaging across all subjects. But I never considered the common use of Dunning-Kruger to be applied to averaged groups of people. It was always brought out for those outliers on the long tail of the novice grout who thought didn’t even know what they didn’t know.
The plot in the blog post seems to be very symmetrical around 50% (to the point where there must be some identity going on). The plot from the paper seems to cross over around 75%. So the 3rd quartile still has some explaining to do, right?
Is any of the raw datasets of such an experiment available? I would like to see a scatter plot of self-assesed score vs actual score instead of the data aggregated into four bins.
Hm... I vaguely remember a different article debunking the Dunning-Kruger. Basically the conclusion was that the data from the experiment shows that people's estimations of their results are all over the place, with people scoring high being actually slightly (but only slightly) more confident that they did well.
While the pop-culture notion of the Dunning-Kruger Effect is "idiots don't know they're idiots," the actual results of the paper were (essentially) that F students thought they were D students, whereas the A students thought they were B students. The argument here seems to be that the original effect is explained as essentially a kind of reversion of the mean argument (people assume themselves to be more average than they are), but I don't entirely buy that--especially since the simulation results they present don't really look like the original Dunning-Kruger results, since the crossover point is in the wrong place, and that's actually kind of significant in the original analysis...
Indeed. The true irony behind the Dunning Kruger Effect is that the pop culture understanding of it is essentially describing itself. Showing overconfidence in areas that you have little clue about.
There are so many strange things about the original Dunning-Kruger plot. Why use quartiles for one axis and percentile for the other? Why use higher precision for the subject's estimate, which is by definition imprecise, and lower precision for the true score, which is known precisely?
I think the only conclusion you can draw from that plot is everyone thinks they'll be in the third quartile.
Regardless of whether Dunning-Kruger is real the solution is the same. DK concerns poor performing people who cannot accurately address their performance relative to a group. Forget DK. The bigger problem is missing objectivity, which is a very real concern. So, just measure for objectivity.
Can they measure things or do they just guess? Are they willing to seek evidence? Even if evidence is immediately available will they use it? Everybody has bias, but is their bias primarily self-oriented?
The consequences for poor objectivity are profound and measurable, but then its an invisible failure for people that struggle with this in the first place. In many industries poor objectivity can result in termination, law suits, criminal penalties, physical harm, and more. Software just seems to pretend this is vapor.
I mean, we all remember the cases where it was true, but do you really think most people think they are good at computer programming? Or speaking Russian? Or playing the harp? Or gardening? In the vast majority of cases, people who are not skilled at something, know that they are not. There are, sure, a few people who are overconfident, but the D-K effect as generally used in conversation was always pretty obviously untrue.
The article may not take into account the possible effect of knowing about the Dunning-Kruger effect (or cultural sayings that goes in a similar direction) may bias measurements. Before it was widely enough known it was not a factor.
Also, negative knowledge comes in two flavours, what you know that you don't know and what you don't know that you don't know. There it may be ground for that effect, but also changes in culture may affect that, specially with exposure to internet/global culture and attitudes, that may make you more aware of what you don't know, and stories of success/fail for taking the wrong approach.
I think it's mostly misapplied. The best example of Dunning-Kruger is an intelligent, competent, Ph.D. in physics thinking 9/11 was faked because "jet fuel can't melt steel", not realizing that steel loses significant tensile strength as it heats up without necessarily melting, which I think most engineers would be aware of. His great knowledge in one area blinds him to his woeful lack of knowledge in another.
I think what people need to realize is that the Dunning-Kruger effect is mostly "not real" because, on average, everyone (regardless of competence) overestimates themselves. Saying that incompetent people overestimate themselves doesn't prove Dunning-Kruger is real, because it doesn't negate the fact that competent people also do this.
I've always found it somewhat ironic that the people who are least familiar with the actual research on the Dunning-Kruger effect tend to be the most confident in discussing it.
This article does not make it's case. He shows a graph of "random" data, and then just kind of keeps going. But that random data is the meat of the whole thing.
Cut out 60% of the useless text, and focus on explaining why random data should look like that.
Agreed. Also it seems like, if the actual test scores and perceived test scores were both sampled iid, the “perceived” line should just be flat, with everyone being at 50th percentile. The fact that the displayed graph deviates from 50 in a way that more resembles the empirical data makes it seem like a cherry-picked random sample
But really, the article seems to be going out of the way to make the author's particular point... but reads to me that the original paper is often understood... it simply shows that "specialists who are very knowledgeable about a subject are more likely to accurately identify gaps in their own knoweldge, when compared to any population less knoweldgeable on the same subject."
For example, I am apparently the most knowledgeable birder in my family. I've taken graduate-level ornithology courses, identify a fair number of N. American birds by their calls, etc. However, I recognize that I know nothing about birds compared to anyone who actually works in the field with them... I don't know enough to even estimate what I don't know.
Having worked in tech my entire life, no amount of research will convince me that the Dunning-Kruger effect is not real. You might as well tell me that this isn't air that I'm breathing.
Do you think there's some element in published research that makes it automagically correct?
Papers saying that lead in petrol was totally safe were "published research", as were the papers saying that replacing tetraethyl lead with benzine made it safer.
Both of those turned out to be pretty majorly wrong, but they were "published research".
This article seems really dumb (no Dunning-Kruger joke intended).
The two lines on the graph are basically linear (for the "actual performance" the quasi-linearity is obvious by the design, for the "estimated performance" it still means that even though dumber people over-estimate their performance, all group still think they do best, when they actually do best, in a relative linear way)
And when they do their simple model (we assume they just generated "real performance" from a gaussian, then added some gaussian noise for the "performance" and another gaussian noise for the "self-assessment") they still (obviously) got two linear graphs that crossed each other.
And then they conclude that this means there is no effect, because "the graphs are eerily similar" (whatever that means)
But obviously the simple model is going to make two lines cross (in particular if you use a min(100, max(0, actual_performance + noise)) since at each extreme, then min and max will tend to skew the line). To put it simply: someone really stupid will STILL not pretend that he's "negatively stupid".
The argument "I can make a simple model without using actual humans which shows some kind of bias that vaguely ressembles the result of a paper" doesn't mean that the actual paper is wrong...
Excessive willful/unwillful ignorance is the root cause of someone exhibiting the Dunning-Kruger Effect. We've all at some point worked or lived with someone with real illusions/delusions about their abilities, and the root of it is ignorance. There's little we can do in our workplaces to mitigate these people. Word of advice from my experience: Never co-found a vc-backed software startup with someone that's done genuine innovation....and been completely ignorant and oblivious about everything else.
Damn if only this article actually explained why you see this effect from random data. Unfortunately it doesn't seem like they understand the maths enough to know. Does anyone fancy reading those papers and giving us a TL;DR?
Self-deception by any other name is still self-deception.
The Dunning-Kruger effect also applies to smart people. You don't stop when you are estimating your ability correctly. As you learn more, you gain more awareness of your ignorance and continue being conservative with your self-estimates.
But overall I think real intelligence by definition requires empathy and humility.
One has to realize that we can't know the things we don't know, which includes the fact that we can't always trust our own beliefs and opinions because we might be relying on faulty or incomplete information, or we might be suffering from a mental health problem, whether we are aware of it or not.
"As a rule, strong feelings about issues do not emerge from deep understanding." -Sloman and Fernbach
Maybe this is reveals more about me than anyone else but the whole usage of dunning-kruger is just another arrow in the quiver for media to talk down to a group that they dismiss because they have different priorities.
I find references to the effect in pop culture are almost always used in an insulting, smug manner.
I feel like the only important point in the article would be to explain how the random data was generated, yet it was relegated to the single sentence: "There was no bias in the coding that would lead these fictitious students to guess they had done really well when their actual score was very low."
Because on the surface, it doesn't make any sense for two sets of "random" numbers between 0-100 selected in pairs to deviate from each other based on whether the first number in the pair was low or not. You would not expect the first number chosen in a pair to influence the second number. Whether the first number was between 0-25 or 76-100, you would expect the second number to be about 50.
So this is obviously some sort of structured randomness that may be entirely justifiable, but the only way to find that out would be to read the two articles that this article purports to summarize for the layman. Instead there's over 1300 words of slop before this sentence, then nearly 700 words of slop after this sentence. Turns out we don't need AI for this. Speaking of random, I don't think that 2000 words is random.
edit: maybe the point of the papers is that low scorers can't underestimate their abilities - as in they literally don't have enough room? If so, that just means that the Dunning-Kruger affect is unavoidable. But the fact is that people are not choosing numbers at random, they are choosing them based on their expectations. People who got zero questions right and expected 100% are as likely as anyone else from a random number generator, and non-existent from actual people.
edit2: OK, I've worked it out. I was making the mistake of thinking that they were evaluating absolute performance rather than relative performance. So each of the first numbers in the pair is unique. But that still leaves the fact that the random draw still predictably sits at 50% where the Dunning-Kruger data is around 65% based on the graph. Seems like norming that with the random data would give you better information.
This makes some sense. If people are asked to guess a number between 1 and 6 and then roll a die, the people who roll low are more likely to overestimate and the people who roll high are more likely to underestimate. But the key is precisely how well random data mimics the effect.
Add negative relationship and negative bias, and the random data will act as intended - hovering randomly around 50%.
But it's not because "some statistical model exhibit a bias that's similar to the result" that this implies "therefore the result is a statistical error"... that's a backward reasonning
Mentioning the Dunning-Krueger effect incorrectly is wonderfully meta...
Software engineers tend to repeat the mantra "you cannot make accurate time estimates". That is true regardless of experience level, and everyone seems to be off by about the same amount. So there we have evidence that people overestimate their skills at every level, and its not that different.
- people not skilled in a thing are bad at estimating their skills
- people are generally bad at estimating their skills
- people skilled in a particular areas often feel they are intellectually fit in other areas
Out of these three I feel like there's some truth in it, at least anecdotally.
"I seem, then, in just this little thing to be wiser than this man at any rate, that what I do not know I do not think I know either."
https://en.wikipedia.org/wiki/I_know_that_I_know_nothing
I can’t remember of DK suggests some sort of effect where the expert has undue self-doubt, though…
This I think is a separate phenomenon, maybe Nobel Disease but there might be a more general term, for example that includes celebrities.
https://en.wiktionary.org/wiki/engineer%27s_disease
And of course there's a relevant xkcd: https://xkcd.com/2501/
I don’t expect it to ever go out of the public consciousness. Like other things that were never real like Stockholm Syndrome I suspect it’s just stuck in the zeitgeist now.
1. https://en.wikipedia.org/wiki/Truthiness
It seems like the overlap between "real psychological effect" and "subtle enough that it requires research to discover" is vanishingly small. I guess that's not really surprising.
It's clear that the perceived curve will be differently sloped, as no one will evaluate themselves as the topmost or the bottommost percentiles, so the edges will be biased.
And if in both cases we draw differences between perceived and actual, we will get the same curve that everyone knows, biased or not.
In the old plot, the bottom quartile has about a 50 percentage point margin between actual and perceived performance while the new one is 30 percentage points, which is a 50% difference between the old and new curve. The second quartile has 3x more margin in the old version relative to the new one.
Source code is here: https://github.com/pem725/Dunning-Kruger (found here: https://pem725.github.io)
Now it's much more clear. The simulated data tries generating the true relationship between actual and perceived scores from 0.0 to 1.0, and bias in self-reporting from 0% to 100%.
So the output graph should be the average of all these data generating processes, yielding perceived relationship around 0.5 and bias around 50%, with some high variation.
If you have the access, run their Shiny code with these values, and you will see the published plot.
I'd argue that this demonstration is much weaker than "making original no more meaningful than random". It's more that the "simulated 50% bias and 0.5 true correlation looks similar to what DK published", which is also far fetched given the data generation they did.
Note: true random (what they were going for) would cover negative relationships, yielding the random true relationship around 0; and if they wouldn't correct the sign of Bias, it would also average at around 0; yielding a realistic "random" with the slope hovering about 50% for any percentile.
The article is baseless and fluffy
I'm at the point honestly, where I don't even consider psychology to be a science anymore.
Besides, deciding what is and isn’t science is a question for the philosophy of science, not science itself.
Physics on the other hand is hard as in unyielding. It is easy to figure out boundaries of physics and map out what is and isn't true, and the few cases were we made a mistake everyone can agree a mistake was made and that formulas needs to be updated since physics is so extremely hard that even a tiny error will get noticed.
Hope that clears it up, physics isn't hard as in difficult, its hard as in rigid. And psychology is soft, not easy.
So, your statement doesn't make sense at all in this discussion, they just said psychology has soft traits, and then you say "ok, so its hard since its soft!". No, soft is difficult, not hard.
So for example, physics is like describing the shape of a metal spoon, and psychology is like describing the shape of a pillow. You can see how describing the shape of the pillow is massively more difficult, because its not fixed, so you have to come up with a language to describe all the ways it can deform and how that would work.
Do you have a source for this?
If a specific novice is over-confident and out of their depth, we say “Dunning-Kruger”
If a specific is under-confident and performing better than their self-estimate, that’s not commonly considered Dunning Kruger, in the colloquial use. It’s called imposter syndrome, or not labeled at all.
The researchers aren’t really disagreeing with that. They found that novices had a wider range of self-estimates of their performance than experienced people. So in the novice group you were more likely to find someone who was grossly over-confident in their abilities, but you also found people who underestimated themselves.
> instead showed that both experts and novices underestimate and overestimate their skills with the same frequency. “It’s just that experts do that over a narrower range,” he wrote to me.
Which doesn’t precisely contradict the idea that among novices you can find people who overestimate their skills. Which is how it’s commonly used.
So I can believe it’s a statistical wash when averaging across all subjects. But I never considered the common use of Dunning-Kruger to be applied to averaged groups of people. It was always brought out for those outliers on the long tail of the novice grout who thought didn’t even know what they didn’t know.
UPD: probably this one https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...
The article in the post is older though
I think the only conclusion you can draw from that plot is everyone thinks they'll be in the third quartile.
Can they measure things or do they just guess? Are they willing to seek evidence? Even if evidence is immediately available will they use it? Everybody has bias, but is their bias primarily self-oriented?
The consequences for poor objectivity are profound and measurable, but then its an invisible failure for people that struggle with this in the first place. In many industries poor objectivity can result in termination, law suits, criminal penalties, physical harm, and more. Software just seems to pretend this is vapor.
> Our results further confirm that experts are more proficient in self-assessing their abilities than novices.
Also, negative knowledge comes in two flavours, what you know that you don't know and what you don't know that you don't know. There it may be ground for that effect, but also changes in culture may affect that, specially with exposure to internet/global culture and attitudes, that may make you more aware of what you don't know, and stories of success/fail for taking the wrong approach.
It's a sort of recursive Dunning-Kruger effect.
Cut out 60% of the useless text, and focus on explaining why random data should look like that.
But really, the article seems to be going out of the way to make the author's particular point... but reads to me that the original paper is often understood... it simply shows that "specialists who are very knowledgeable about a subject are more likely to accurately identify gaps in their own knoweldge, when compared to any population less knoweldgeable on the same subject."
For example, I am apparently the most knowledgeable birder in my family. I've taken graduate-level ornithology courses, identify a fair number of N. American birds by their calls, etc. However, I recognize that I know nothing about birds compared to anyone who actually works in the field with them... I don't know enough to even estimate what I don't know.
Papers saying that lead in petrol was totally safe were "published research", as were the papers saying that replacing tetraethyl lead with benzine made it safer.
Both of those turned out to be pretty majorly wrong, but they were "published research".
The two lines on the graph are basically linear (for the "actual performance" the quasi-linearity is obvious by the design, for the "estimated performance" it still means that even though dumber people over-estimate their performance, all group still think they do best, when they actually do best, in a relative linear way)
And when they do their simple model (we assume they just generated "real performance" from a gaussian, then added some gaussian noise for the "performance" and another gaussian noise for the "self-assessment") they still (obviously) got two linear graphs that crossed each other.
And then they conclude that this means there is no effect, because "the graphs are eerily similar" (whatever that means)
But obviously the simple model is going to make two lines cross (in particular if you use a min(100, max(0, actual_performance + noise)) since at each extreme, then min and max will tend to skew the line). To put it simply: someone really stupid will STILL not pretend that he's "negatively stupid".
The argument "I can make a simple model without using actual humans which shows some kind of bias that vaguely ressembles the result of a paper" doesn't mean that the actual paper is wrong...
That being said I loved the mercury/Glasgow explanation. Anecdotally I see that all the time.
Self-deception by any other name is still self-deception.
The Dunning-Kruger effect also applies to smart people. You don't stop when you are estimating your ability correctly. As you learn more, you gain more awareness of your ignorance and continue being conservative with your self-estimates.
But overall I think real intelligence by definition requires empathy and humility.
One has to realize that we can't know the things we don't know, which includes the fact that we can't always trust our own beliefs and opinions because we might be relying on faulty or incomplete information, or we might be suffering from a mental health problem, whether we are aware of it or not.
"As a rule, strong feelings about issues do not emerge from deep understanding." -Sloman and Fernbach
I find references to the effect in pop culture are almost always used in an insulting, smug manner.
Because on the surface, it doesn't make any sense for two sets of "random" numbers between 0-100 selected in pairs to deviate from each other based on whether the first number in the pair was low or not. You would not expect the first number chosen in a pair to influence the second number. Whether the first number was between 0-25 or 76-100, you would expect the second number to be about 50.
So this is obviously some sort of structured randomness that may be entirely justifiable, but the only way to find that out would be to read the two articles that this article purports to summarize for the layman. Instead there's over 1300 words of slop before this sentence, then nearly 700 words of slop after this sentence. Turns out we don't need AI for this. Speaking of random, I don't think that 2000 words is random.
edit: maybe the point of the papers is that low scorers can't underestimate their abilities - as in they literally don't have enough room? If so, that just means that the Dunning-Kruger affect is unavoidable. But the fact is that people are not choosing numbers at random, they are choosing them based on their expectations. People who got zero questions right and expected 100% are as likely as anyone else from a random number generator, and non-existent from actual people.
edit2: OK, I've worked it out. I was making the mistake of thinking that they were evaluating absolute performance rather than relative performance. So each of the first numbers in the pair is unique. But that still leaves the fact that the random draw still predictably sits at 50% where the Dunning-Kruger data is around 65% based on the graph. Seems like norming that with the random data would give you better information.