In this commentary I explain how mean differences between normal distributions give rise to different percentages of the populations being above or below a given threshold, depending on where the threshold is.


Research uncovers flawed IQ scoring system” is the headline on phys.org, which often posts news about research from other fields. It concerns a study by Harrison et al (2015). The researchers have allegedly “uncovered anomalies and issues with the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV), one of the most widely used intelligence tests in the world”. An important discovery, if true. Let’s hear it from the lead researcher:

“Looking at the normal distribution of scores, you’d expect that only about five per cent of the population should get an IQ score of 75 or less,” says Dr. Harrison. “However, while this was true when we scored their tests using the American norms, our findings showed that 21 per cent of college and university students in our sample had an IQ score this low when Canadian norms were used for scoring.”

How can it be? To learn more, we delve into the actual paper titled: Implications for Educational Classification and Psychological Diagnoses Using the Wechsler Adult Intelligence Scale–Fourth Edition With Canadian Versus American Norms.

The paper

First they summarize a few earlier studies on Canada and the US. The Canadians obtained higher raw scores. Of course, this was hypothesized to be due to differences in ethnicity and educational achievement factors. However, this did not quite work out, so Harrison et al decided to investigate it more (they had already done so in 2014). Their method consists of taking the scores from a large mixed sample consisting of healthy people — i.e. with no diagnosis, 11% — and people with various mental disorders (e.g. 53.5% with ADHD), and then scoring this group on both the American and the Canadian norms. What did they find?

Blast! The results were similar to the results from the previous standardization studies! What happened? To find out, Harrison et al do a thorough examination of various subgroups in various ways. No matter which age group they compare, the result won’t go away. They also report the means and Cohen’s d for each subtest and aggregate measure — very helpful. I reproduce their Table 1 below:

Score M (US)
p d r
FSIQ 95.5 12.9 88.1 14.4 <.001 0.54 0.99
GAI 98.9 14.7 92.5 16 <.001 0.42 0.99
Index Scores
Verbal Comprehension 97.9 15.1 91.8 16.3 <.001 0.39 0.99
Perceptual Reasoning 99.9 14.1 94.5 15.9 <.001 0.36 0.99
Working Memory 90.5 12.8 83.5 13.8 <.001 0.53 0.99
Processing Speed 95.2 12.9 90.4 14.1 <.001 0.36 0.99
Subtest Scores
Verbal Subtests
Vocabulary 9.9 3.1 8.7 3.3 <.001 0.37 0.99
Similarities 9.7 3 8.5 3.3 <.001 0.38 0.98
Information 9.2 3.1 8.5 3.3 <.001 0.22 0.99
Arithmetic 8.2 2.7 7.4 2.7 <.001 0.3 0.99
Digit Span 8.4 2.5 7.1 2.7 <.001 0.5 0.98
Performance Subtests
Block Design 9.8 3 8.9 3.2 <.001 0.29 0.99
Matrix Reasoning 9.8 2.9 9.1 3.2 <.001 0.23 0.99
Visual Puzzles 10.5 2.9 9.4 3.1 <.001 0.37 0.99
Symbol Search 9.3 2.8 8.5 3 <.001 0.28 0.99
Coding 8.9 2.5 8.2 2.6 <.001 0.27 0.98


Sure enough, the scores are lower using the Canadian norms. And very ‘significant’ too. A mystery.

Next, they go on to note how this sometimes changes the classification of individuals into 7 arbitrarily chosen intervals of IQ scores, and how this differs between subtests. They spend a lot of e-ink noting percents about this or that classification. For instance:

“Of interest was the percentage of individuals who would be classified as having a FSIQ below the 10th percentile or who would fall within the IQ range required for diagnosis of ID (e.g., 70 ± 5) when both normative systems were applied to the same raw scores. Using American norms, 13.1% had an IQ of 80 or less, and 4.2% had an IQ of 75 or less. By contrast, when using Canadian norms, 32.3% had an IQ of 80 or less, and 21.2% had an IQ of 75 or less.”

I wonder if some coherent explanation can be found for all these results. In their discussion they ask:

“How is it that selecting Canadian over American norms so markedly lowers the standard scores generated from the identical raw scores? One possible explanation is that more extreme scores occur because the Canadian normative sample is smaller than the American (cf. Kahneman, 2011).”

If the reader was unsure, yes, this is Kahneman’s 2011 book about cognitive biases and dual process theory.

They have more suggestions about the reason:

“One cannot explain this difference simply by saying it is due to the mature students in the sample who completed academic upgrading, as the score differences were most prominent in the youngest cohorts. It is difficult to explain these findings simply as a function of disability status, as all participants were deemed otherwise qualified by these postsecondary institutions (i.e., they had met normal academic requirements for entry into regular postsecondary programs). Furthermore, in Ontario, a diagnosis of LD is given only to students with otherwise normal thinking and reasoning skills, and so students with such a priori diagnosis would have had otherwise average full scale or general abilities scores when tested previously. Performance exaggeration seems an unlikely cause for the findings, as the students’ scores declined only when Canadian norms were applied. Finally, although no one would argue that a subset of disabled students might be functioning below average, it is difficult to believe that almost half of these postsecondary students would fall in this IQ range given that they had graduated from high school with marks high enough to qualify for acceptance into bona fide postsecondary programs. Whatever the cause, our data suggest that one must question both the representativeness of the Canadian normative sample in the younger age ranges and the accuracy of the scores derived when these norms are applied.”

And finally they conclude with a recommendation not to use the Canadian norms for Canadians because this results in lower IQs:

Overall, our findings suggest a need to examine more carefully the accuracy and applicability of the WAIS-IV Canadian norms when interpreting raw test data obtained from Canadian adults. Using these norms appears to increase the number of young adults identified as intellectually impaired and could decrease the number who qualify for gifted programming or a diagnosis of LD. Until more research is conducted, we strongly recommend that clinicians not use Canadian norms to determine intellectual impairment or disability status. Converting raw scores into Canadian standard scores, as opposed to using American norms, systematically lowers the scores of postsecondary students below the age of 35, as the drop in FSIQ was higher for this group than for older adults. Although we cannot know which derived scores most accurately reflect the intellectual abilities of young Canadian adults, it certainly seems implausible that almost half of postsecondary students have FSIQ scores below the 16th percentile, calling into question the accuracy of all other derived WAIS-IV Canadian scores in the classification of cognitive abilities.

Are you still wondering what it going on?

Populations with different mean IQs and cut-offs

Harrison et al seems to have inadvertently almost rediscovered the fact that Canadians are smarter than Americans. They don’t quite make it to this point even when faced with obvious and strong evidence (multiple standardization samples). They somehow don’t realize that using the norms from these standardization samples will reproduce the differences found in those samples, and won’t really report anything new.

Their numerous differences in percents reaching this or that cut-off are largely or entirely explained by simple statistics. They have two populations which have an IQ difference of 7.4 points (95.5 – 88.1 from Table 1) or 8.1 points (15 * .54 d from Table 1). Now, if we plot these (I used a difference of 7.5 IQ) and choose some arbitrary cut-offs, like those between arbitrarily chosen intervals, we see something like this:


Except that I cheated and chose all the cut-offs. The brown and the green lines are the ratios between the densities (read off the second y-axis). We see that around 100, they are generally low, but as we get further from the means, they get a lot larger. This simple fact is not generally appreciated. It’s not a new problem, Arthur Jensen spent much of a chapter in his behemoth 1980 book on the topic, he quotes for instance:

“In the construction trades, new apprentices were 87 percent white and 13 percent black. [Blacks constitute 12 percent of the U.S. population.] For the Federal Civil Service, of those employees above the GS-5 level, 88.5 percent were white, 8.3 percent black, and women account for 30.1 of all civil servants. Finally, a 1969 survey of college teaching positions showed whites with 96.3 percent of all posi­ tions. Blacks had 2.2 percent, and women accounted for 19.1 percent. (U.S. Commission on Civil Rights, 1973)”

Sounds familiar? Razib Khan has also written about it. Now, let’s go back to one of the quotes:

“Using American norms, 13.1% had an IQ of 80 or less, and 4.2% had an IQ of 75 or less. By contrast, when using Canadian norms, 32.3% had an IQ of 80 or less, and 21.2% had an IQ of 75 or less. Most notably, only 0.7% (2 individuals) obtained a FSIQ of 70 or less using American norms, whereas 9.7% had IQ scores this low when Canadian norms were used. At the other end of the spectrum, 1.4% of the students had FSIQ scores of 130 or more (gifted) when American norms were used, whereas only 0.3% were this high using Canadian norms.”

We can put these in a table and calculate the ratios:

IQ threshold Percent US Percent CAN US/CAN CAN/US
130 1.4 0.3 4.67 0.21
80 13.1 32.3 0.41 2.47
75 4.2 21.2 0.20 5.05
70 0.7 9.7 0.07 13.86


And we can also calculate the expected values based on the two populations (with means of 95.5 and 88) above:

IQ threshold Percent US Percent CAN US/CAN CAN/US
130 1.07 0.26 4.12 0.24
80 15.07 29.69 0.51 1.97
75 8.59 19.31 0.44 2.25
70 4.46 11.51 0.39 2.58


This is fairly close right? The only outlier (in italic) is the much lower than expected value for <70 IQ using US norms, perhaps a sampling error. But overall, this is a pretty good fit to the data. Perhaps we have our explanation.

What about those (mis)classification values in their Table 2? Well, for similar reasons that I won’t explain in detail, these are simply a function of the difference between the groups in that variable, e.g. Cohen’s d. In fact, if we correlate the d vector and the “% within same classification” we get a correlation of -.95 (-.96 using rank-orders).

MCV analysis

Incidentally, the d values report in their Table 1 are useful for using the method of correlated vectors. In a previous study comparing US and Canadian IQ data, Dutton and Lynn (2014) compared WAIS-IV standardization data. They found a mean difference of .31 d, or 4.65 IQ, which was reduced to 2.1 IQ if the samples were matched on education, ethnicity and sex. An interesting thing was that the difference between the countries was largest on the most g-loading subtests. When this happens, it is called a Jensen effect (or that it has a positive Jensen coefficient, Kirkegaard 2014). The value in their study was .83, which is on the high side (see e.g. te Nijenhuis et al, 2015).

I used the same loadings as used in their study (McFarland, 2013), and found a correlation of .24 (.35 with rank-order), substantially weaker.

Supplementary material

The R code and data files can be found in the Open Science Framework repository.


  • Harrison, A. G., Holmes, A., Silvestri, R., Armstrong, I. T. (2015). Implications for Educational Classification and Psychological Diagnoses Using the Wechsler Adult Intelligence Scale–Fourth Edition With Canadian Versus American Norms. Journal of Psychoeducational Assessment. 1-13.
  • Jensen, A. R. (1980). Bias in Mental Testing.
  • Kirkegaard, E. O. (2014). The personal Jensen coefficient does not predict grades beyond its association with g. Open Differential Psychology.
  • McFarland, D. (2013). Model individual subtests of the WAIS IV with multiple latent
    factors. PLoSONE. 8(9): e74980. doi:10.1371/journal.pone.0074980
  • te Nijenhuis, J., van den Hoek, M., & Armstrong, E. L. (2015). Spearman’s hypothesis and Amerindians: A meta-analysis. Intelligence, 50, 87-92.

It is a long time ago since I did this project. I did not write about it here before but it is a pity since the results are thus not ‘out there’. I put the project page here in 2012 (!). In short, I wrote python code to crawl Wikipedia lists. I figured out a way to decide whether a person was male or female. This was done using gendered pronouns which exist in English. I.e., the crawler fetches the full-text of the article, and counts “he”, “his”, “him”, “she”, “her”. It assigns the gender with the most pronouns. This method seems rather reliable in my informal testing.

I specifically wrote it to look at comedians because I had read a study of comedians (Greengross et al 2012). They gave personality and a vocabulary test (from the Multidimensional Aptitude Battery, r=.62 with WAIS-R) to a sample of 31 comedians and psychology 400 students. The comedians scored 1.34 d above the students. Some care must be taken with this result. The comedians were much older and vocabulary raw scores go up with age (mean age 38.9 vs. 20.5). The authors do not state that they were age-corrected. Psychology students are not very bright and this was a sample from New Mexico with lots of Hispanics. We can safely conclude that comedians are smarter than the student body and the general population of New Mexico, but can’t say much about exactly. We can hazard a guess at student body (maybe 107 IQ) + age corrected d (maybe 15 IQ), so we end with an estimate of 122 IQ.

There are various other tables of interest that don’t need much explaining, which I will paste below:


As of writing this, I found another older study (Janus, 1975). I will just quote:

The data to support the above theses were gathered through psychological case studies, in-depth interviews with many of the leading comedians in the United States today, and psychological tests. [n addition to a clinical interview, the instruments used were the Wechsler Adult Intelligence Scale, Machover Human Figure Drawing Test, graphological analysis, earliest memories, and recurring dreams.

Population consisted of 55 professional comedians. In order to be considered in this study, comedians had to be full-time professional stand-up comedians. Most of the subjects earned salaries of six figures or over, from comedy alone. In order to make the sample truly representative, each comedian had to be nationally known and had to have been in the field full time for at least ten years. The average time spent in full- time comedy for the subjects was twenty-five years. The group consisted of fifity-one men and four women. They represented all major religions, many geographic areas, and diverse socioeconomic backgrounds. Comedians were interviewed in New York, California, and points in between. Their socioeconomic backgrounds, family hierarchy, demographic information, religious influences, and analytic material were investigated. Of the population researched, 85 percent came from lower-class homes, 10 percent from lower-middle-class homes, and 5 percent from middle-class and upper-middle-class homes. All subjects participated voluntarily, received no remuneration, and were personally interviewed by the author.

I.Q. scores ranged from 115 to 160+. For a population at large, I.Q. scores in the average range are from 90 to 110. I.Q. scores in the bright-average range of intelligence, that is, from 10g to 115, were scored by only three subjects. The remainder scored above 125, with the mean score being 138. The vocabulary subtest was utilized. Several subjects approached it as a word-association test, but all regarded it as a challenge. Since these are verbal people, they were highly motivated. The problem was not one of getting them to respond, it was one of continuously allaying their anxiety, and re- assuring them they they were indeed doing well.

So, a very high mean was found. WAIS was published in 1955, so there is approximately 20 years of FLynn gains in raw scores, presumably uncorrected for. According to a new meta-analysis of FLynn gains (Trahan et al 2014), the mean gain is 2.31 per decade. So we are assuming about a gain of 4.6 IQ here. But then again, the verbal test for the students was published in 1984, so there may be some gain there as well (FLynn effects supposedly showed down recent in Western countries). Perhaps a net gain in favor of the old study by 4 IQ. In that case, we get estimates of 134 and 122. With samples of 31 and 55, different subtests, sampling procedure etc., this is surely reasonable. We can take a weighted mean and say best estimate for professional comedians is about 129.7, or about +2SD. It seems a bit wild, are comedians really on average as smart as fysicists?

EDIT: There is another study by Janus (1978). Same test:

[N=14] Intelligence scores ranged from 112 to 144 plus. (The range of average IQ is from 90 to 110.) Four subjects scored in the bright average range–i.e., 108 to 115. The remaining subjects scored above 118 with a mean score of 126. Two subjects scored above 130. The mean score for male comics was 138. The subjects approached the testing with overenthusiasm, in some cases bordering on frenzy. Despite the brightness of the group, all subjects needed constant reassurance and positive feedback.

So 126, with ~5 IQ because of FLynn effect. New weighted mean is 128.5 IQ.

Perhaps we should test it. If you want to test it with me, write me an email/tweet. We will design a questionnaire and give it to your local sample of comedians. One can e.g. try to convince professional comedian organizations (e.g. Danish here, N=35) to forward it to their members.

So what did I find?

I did the scraping twice. One time at first in 2012, and then again later when I was reminded of the project in May 2014. Now I have been reminded of it again. The very basic stats is that there were 1106 comedians found, of which the gender distribution was this (the “other” is unknown gender, which was 1 person).

What about the change over time? The code fetches their birth year if mentioned on their Wikipedia page. Then I limited the data to US comedians (66% of the sample). This was done because if we are looking for ways to explain it, we need to restrict ourselves to some more homogenous subset. What explains the change in gender distribution in Saudi Arabia at time t1 may not also explain it in Japan.

Next we get a common scientific conflict of interest: that between precision of estimate and detail. Essentially what we need is a moving average since most or all years have too few comedians for a reliable estimate (very zigzaggy lines on the plot). So we must decide how large a moving average to use. A larger will give more precision in estimate, but less detail. I decided to try a few different options (5, 10, 15, 20). To avoid extreme zigzagginess, I only plotted them if there were >=20 persons in the interval. This plots look like this:

So in general we see a decline in the proportion of male comedians. But it is not not going straight down. There is a local minimum in 1960 or so, and a local maximum in 1980 or so. How to explain these?

I tried abortion rate (not much data before 1973) and total fertility rate (plenty of data) but was not convinced by the results. One can also inflate or deflate the numbers according to which moving interval one chooses. One can even try all the possible sizes of intervals and the delays to see which gives the best match. I did some of this semi-manually using spreadsheets, but it has a very high chance of overfitting. One would need to do some programming to try all of them in a reasonable time.

I wrote some of this stuff in a paper, but never finished it. It can now be found at its OSF repository.


Newer dataset from May 2014.

Older dataset dated to 2012.

Python code. This includes code to crawl Wikipedia with and quite a lot of other raw data output files.


Greengross, G., Martin, R. A., & Miller, G. (2012). Personality traits, intelligence, humor styles, and humor production ability of professional stand-up comedians compared to college students. Psychology of Aesthetics, Creativity, and the Arts, 6(1), 74.

Janus, S. S. (1975). The great comedians: Personality and other factors. The American Journal of Psychoanalysis, 35(2), 169-174.

Janus, S. S., Bess, B. E., & Janus, B. R. (1978). The great comediennes: Personality and other factors. The American Journal of Psychoanalysis, 38(4), 367-372.

Trahan, L. H., Stuebing, K. K., Fletcher, J. M., & Hiscock, M. (2014). The Flynn effect: A meta-analysis.

If a person is waiting to be treated at a hospital and he complains about waiting too long… is he being impatient?

[14:40:05] Emil – Deleet: is it funny to talk about a sex division of labor?
[14:40:39] Emil – Deleet: meaning #1: Effort expended on a particular task; toil, work.
meaning #2: The act of a mother giving birth.


I was looking for something else.. and found this instead… From here: able2know.org/topic/151812-1


I think a 9 year old killing himself is a good representation of suicide as a whole. Selfish,short-sighted, and always looking for an escape. Although it seems insensitive for me to say this, I think we just need to realize that those things are apart of deciding to end your life.


It is funny that people who stay alive because they want to, call people “selfish” who kill themselves because they want to. (It is like those who have children because they want children calling childless couples selfish for not having children because they don’t want to have children.)

And it is absurd to call a solution to all of life’s problems forever a “short-sighted” solution. The 9 year old could not possibly have come up with any other solution to his problems that would have been so complete and long lasting.


[11:19:25] Jens Arhøj – Strawb: Tfw reading Game of Thrones
>The author doesn’t use commas as often as I would like
>I really pick the wrong things to focus on
[11:19:34] Emil – Deleet: yes
[11:19:36 | Edited 11:19:52] Emil – Deleet: commas, suck
[11:19:55 | Edited 11:20:06] Jens Arhøj – Strawb: Damn, you


Richard Feynman Surely Youre Joking Mr Feynman v5 ebook download free pdf


this is a fun, easy to read book. i was told to read it by a friend. i read it to avoid doing the linguistics tests im supposed to do. useful procrastination ftw!


As usual, comments and quotes below



Another thing I did in high school was to invent problems and theorems. I mean, if I were doing

any mathematical thing at all, I would find some practical example for which it would be useful. I

invented a set of right-triangle problems. But instead of giving the lengths of two of the sides to

find the third, I gave the difference of the two sides. A typical example was: There’s a flagpole, and

there’s a rope that comes down from the top. When you hold the rope straight down, it’s three feet

longer than the pole, and when you pull the rope out tight, it’s five feet from the base of the pole.

How high is the pole?


tricky, but certainly doable for primary school children. the smart of them. im fairly certain that a lot of high school students wud not be able to solve this.



I tried to explain–it was my own aunt–that there was no reason not to do that, but you can’t say

that to anybody who’s smart, who runs a hotel! I learned there that innovation is a very difficult

thing in the real world.


truth! this is politics in a nutshell, any kind of politics: national, local, office…



The other guy’s afraid, so he says no. So I take the two girls in a taxi to the hotel, and discover

that there’s a dance organized by the deaf and dumb, believe it or not. They all belonged to a club.

It turns out many of them can feel the rhythm enough to dance to the music and applaud the band at

the end of each number.


It was very, very interesting! I felt as if I was in a foreign country and couldn’t speak the

language: I could speak, but nobody could hear me. Everybody was talking with signs to everybody

else, and I couldn’t understand anything! I asked my girl to teach me some signs and I learned a few,

like you learn a foreign language, just for fun.


Everyone was so happy and relaxed with each other, making jokes and smiling all the time; they

didn’t seem to have any real difficulty of any kind communicating with each other. It was the same

as with any other language, except for one thing: as they’re making signs to each other, their heads

were always turning from one side to the other. I realized what that was. When someone wants to

make a side remark or interrupt you, he can’t yell, “Hey, Jack!” He can only make a signal, which

you won’t catch unless you’re in the habit of looking around all the time.


never thought of that, but true!



When it came time for me to give my talk on the subject, I started off by drawing an outline of

the cat and began to name the various muscles.

The other students in the class interrupt me: “We know all that!”

“Oh,” I say, “you do? Then no wonder I can catch up with you so fast after you’ve had four years

of biology.” They had wasted all their time memorizing stuff like that, when it could be looked up

in fifteen minutes.


ive heard this complaint lots of time about biology. i rather like evolutionary biology, which surely cannot be learned in 15 mins, but i dunno abouy plant cell biology or whatever. is biology mostly just remembering stuff? surely things like genetics, pop* genetics, evolutionary theory are hard.



At the Princeton graduate school, the physics department and the math department shared a

common lounge, and every day at four o’clock we would have tea. It was a way of relaxing in the

afternoon, in addition to imitating an English college. People would sit around playing Go, or

discussing theorems. In those days topology was the big thing.

I still remember a guy sitting on the couch, thinking very hard, and another guy standing in front

of him, saying, “And therefore such-and-such is true.”


“Why is that?” the guy on the couch asks.


“It’s trivial! It’s trivial!” the standing guy says, and he rapidly reels off a series of logical steps:

“First you assume thus-and-so, then we have Kerchoff’s this-and-that; then there’s Waffenstoffer’s

Theorem, and we substitute this and construct that. Now you put the vector which goes around here

and then thus-and-so . . .” The guy on the couch is struggling to understand all this stuff, which

goes on at high speed for about fifteen minutes!


Finally the standing guy comes out the other end, and the guy on the couch says, “Yeah, yeah.

It’s trivial.”


We physicists were laughing, trying to figure them out. We decided that “trivial” means

“proved.” So we joked with the mathematicians: “We have a new theorem–that mathematicians can

prove only trivial theorems, because every theorem that’s proved is trivial.”


i thought of that befor. it makes certain theories of tautologies rather implausible. if tautologies, or necessary truths are all trivial, and just restating things – why arent they all obvius? …



One thing I never did learn was contour integration. I had learned to do integrals by various

methods shown in a book that my high school physics teacher Mr. Bader had given me.


One day he told me to stay after class. “Feynman,” he said, “you talk too much and you make

too much noise. I know why. You’re bored. So I’m going to give you a book. You go up there in the

back, in the corner, and study this book, and when you know everything that’s in this book, you can

talk again.”


i wish my teachers wud hav don that to me! or that i had grown up with Khan academy!



In another experiment, I laid out a lot of glass microscope slides, and got the ants to walk on

them, back and forth, to some sugar I put on the windowsill. Then, by replacing an old slide with a

new one, or by rearranging the slides, I could demonstrate that the ants had no sense of geometry:

they couldn’t figure out where something was. If they went to the sugar one way and there was a

shorter way back, they would never figure out the short way.

It was also pretty clear from rearranging the glass slides that the ants left some sort of trail. So

then came a lot of easy experiments to find out how long it takes a trail to dry up, whether it can be

easily wiped off, and so on. I also found out the trail wasn’t directional. If I’d pick up an ant on a

piece of paper, turn him around and around, and then put him back onto the trail, he wouldn’t know

that he was going the wrong way until he met another ant. (Later, in Brazil, I noticed some leaf-

cutting ants and tried the same experiment on them. They could tell, within a few steps, whether

they were going toward the food or away from it–presumably from the trail, which might be a

series of smells in a pattern: A, B, space, A, B, space, and so on.)

I tried at one point to make the ants go around in a circle, but I didn’t have enough patience to set

it up. I could see no reason, other than lack of patience, why it couldn’t be done.


yes, that DOES happen by accident in nature.




So Frankel figured out a nice program. If we got enough of these machines in a room, we could

take the cards and put them through a cycle. Everybody who does numerical calculations now

knows exactly what I’m talking about, but this was kind of a new thing then–mass production with

machines. We had done things like this on adding machines. Usually you go one step across, doing

everything yourself. But this was different–where you go first to the adder, then to the multiplier,

then to the adder, and so on. So Frankel designed this system and ordered the machines from the

IBM company because we realized it was a good way of solving our problems.


We needed a man to repair the machines, to keep them going and everything. And the army was

always going to send this fellow they had, but he was always delayed. Now, we always were in a

hurry. Everything we did, we tried to do as quickly as possible. In this particular case, we worked

out all the numerical steps that the machines were supposed to do–multiply this, and then do this,

and subtract that. Then we worked out the program, but we didn’t have any machine to test it on. So

we set up this room with girls in it. Each one had a Marchant: one was the multiplier, another was

the adder. This one cubed–all she did was cube a number on an index card and send it to the next



We went through our cycle this way until we got all the bugs out. It turned out that the speed at

which we were able to do it was a hell of a lot faster than the other way where every single person

did all the steps. We got speed with this system that was the predicted speed for the IBM machine.

The only difference is that the IBM machines didn’t get tired and could work three shifts. But the

girls got tired after a while.


aka. en.wikipedia.org/wiki/Assembly_line



Well, Mr. Frankel, who started this program, began to suffer from the computer disease that

anybody who works with computers now knows about. It’s a very serious disease and it interferes

completely with the work. The trouble with computers is you play with them. They are so

wonderful. You have these switches–if it’s an even number you do this, if it’s an odd number you

do that–and pretty soon you can do more and more elaborate things if you are clever enough, on

one machine.





All during the war, and even after, there were these perpetual rumors: “Somebody’s been trying

to get into Building Omega!” You see, during the war they were doing experiments for the bomb in

which they wanted to get enough material together for the chain reaction to just get started. They

would drop one piece of material through another, and when it went through, the reaction would

start and they’d measure how many neutrons they got. The piece would fall through so fast that

nothing should build up and explode. Enough of a reaction would begin, however, so they could

tell that things were really starting correctly, that the rates were right, and everything was going

according to prediction–a very dangerous experiment!


O_o, very dangerus experiment indeed!



That evening I went for a walk in town, and came upon a small crowd of people standing around

a great big rectangular hole in the road–it had been dug for sewer pipes, or something–and there,

sitting exactly in the hole, was a car. It was marvelous: it fitted absolutely perfectly, with its roof

level with the road. The workmen hadn’t bothered to put up any signs at the end of the day, and the

guy had simply driven into it. I noticed a difference: When we’d dig a hole, there’d be all kinds of

detour signs and flashing lights to protect us. There, they dig the hole, and when they’re finished for

the day, they just leave.





The meeting in Japan was in two parts: one was in Tokyo, and the other was in Kyoto. In the bus

on the way to Kyoto I told my friend Abraham Pais about the Japanese-style hotel, and he wanted

to try it. We stayed at the Hotel Miyako, which had both American-style and Japanese-style rooms,

and Pais shared a Japanese-style room with me.


The next morning the young woman taking care of our room fixes the bath, which was right in

our room. Sometime later she returns with a tray to deliver breakfast. I’m partly dressed. She turns

to me and says, politely, “Ohayo, gozai masu,” which means, “Good morning.”

Pais is just coming out of the bath, sopping wet and completely nude. She turns to him and with

equal composure says, “Ohayo, gozai masu,” and puts the tray down for us.

Pais looks at me and says, “God, are we uncivilized!”


We realized that in America if the maid was delivering breakfast and the guy’s standing there,

stark naked, there would be little screams and a big fuss. But in Japan they were completely used to

it, and we felt that they were much more advanced and civilized about those things than we were.


stupid puritanism and fear of nakedness.



There was a sociologist who had written a paper for us all to read–something he had written

ahead of time. I started to read the damn thing, and my eyes were coming out: I couldn’t make head

nor tail of it! I figured it was because I hadn’t read any of the books on that list. I had this uneasy

feeling of “I’m not adequate,” until finally I said to myself, “I’m gonna stop, and read one sentence

slowly, so I can figure out what the hell it means.”

So I stopped–at random–and read the next sentence very carefully. I can’t remember it precisely,

but it was very close to this: “The individual member of the social community often receives his

information via visual, symbolic channels.” I went back and forth over it, and translated. You know

what it means? “People read.”


Then I went over the next sentence, and I realized that I could translate that one also. Then it

became a kind of empty business: “Sometimes people read; sometimes people listen to the radio,”

and so on, but written in such a fancy way that I couldn’t understand it at first, and when I finally

deciphered it, there was nothing to it.


There was only one thing that happened at that meeting that was pleasant or amusing. At this

conference, every word that every guy said at the plenary session was so important that they had a

stenotypist there, typing every goddamn thing. Somewhere on the second day the stenotypist came

up to me and said, “What profession are you? Surely not a professor.”

“I am a professor,” I said.

“Of what?”

“Of physics–science.”

“Oh! That must be the reason,” he said.

“Reason for what?” He said, “You see, I’m a stenotypist, and I type everything that is said here. Now, when the other

fellas talk, I type what they say, but I don’t understand what they’re saying. But every time you get

up to ask a question or to say something, I understand exactly what you mean–what the question is,

and what you’re saying–so I thought you can’t be a professor!”


yes, it is mor difficult to say somthing clearly than to obscure it.



There was a special dinner at some point, and the head of the theology place, a very nice, very

Jewish man, gave a speech. It was a good speech, and he was a very good speaker, so while it

sounds crazy now, when I’m telling about it, at that time his main idea sounded completely obvious

and true. He talked about the big differences in the welfare of various countries, which cause

jealousy, which leads to conflict, and now that we have atomic weapons, any war and we’re

doomed, so therefore the right way out is to strive for peace by making sure there are no great

differences from place to place, and since we have so much in the United States, we should give up

nearly everything to the other countries until we’re all even. Everybody was listening to this, and

we were all full of sacrificial feeling, and all thinking we ought to do this. But I came back to my

senses on the way home.


The next day one of the guys in our group said, “I think that speech last night was so good that

we should all endorse it, and it should be the summary of our conference.”

I started to say that the idea of distributing everything evenly is based on a theory that there’s

only X amount of stuff in the world, that somehow we took it away from the poorer countries in the

first place, and therefore we should give it back to them. But this theory doesn’t take into account

the real reason for the differences between countries–that is, the development of new techniques

for growing food, the development of machinery to grow food and to do other things, and the fact

that all this machinery requires the concentration of capital. It isn’t the stuff, but the power to make

the stuff, that is important. But I realize now that these people were not in science; they didn’t

understand it. They didn’t understand technology; they didn’t understand their time.


sounds like sorryaboutcolonialism (see www.everythingisaremix.info/everything-is-a-remix-part-2-transcript/).


these inequalities ar ther becus of ppl ar unequal to begin with. even if we redistributed wealth, it wudnt take long b4 whites and asians were superior again.



Once I was asked to serve on a committee which was to evaluate various weapons for the army,

and I wrote a letter back which explained that I was only a theoretical physicist, and I didn’t know

anything about weapons for the army.


The army responded that they had found in their experience that theoretical physicists were very

useful to them in making decisions, so would I please reconsider?

I wrote back again and said I didn’t really know anything, and doubted I could help them.

Finally I got a letter from the Secretary of the Army, which proposed a compromise: I would

come to the first meeting, where I could listen and see whether I could make a contribution or not.

Then I could decide whether I should continue.

I said I would, of course. What else could I do?

I went down to Washington and the first thing that I went to was a cocktail party to meet

everybody. There were generals and other important characters from the army, and everybody

talked. It was pleasant enough.


One guy in a uniform came to me and told me that the army was glad that physicists were

advising the military because it had a lot of problems. One of the problems was that tanks use up

their fuel very quickly and thus can’t go very far. So the question was how to refuel them as they’re

going along. Now this guy had the idea that, since the physicists can get energy out of uranium,

could I work out a way in which we could use silicon dioxide–sand, dirt–as a fuel? If that were

possible, then all this tank would have to do would be to have a little scoop underneath, and as it

goes along, it would pick up the dirt and use it for fuel! He thought that was a great idea, and that

all I had to do was to work out the details. That was the kind of problem I thought we would be

talking about in the meeting the next day.


i wonder… ar they still so depressingly dumb?



This question of trying to figure out whether a book is good or bad by looking at it carefully or

by taking the reports of a lot of people who looked at it carelessly is like this famous old problem:

Nobody was permitted to see the Emperor of China, and the question was, What is the length of the

Emperor of China’s nose? To find out, you go all over the country asking people what they think

the length of the Emperor of China’s nose is, and you average it. And that would be very “accurate”

because you averaged so many people. But it’s no way to find anything out; when you have a very

wide range of people who contribute without looking carefully at it, you don’t improve your

knowledge of the situation by averaging.


F seems to be wrong, but he might hav a point about the conditions under which wisdom of the crowds averaging works. en.wikipedia.org/wiki/Wisdom_of_the_crowd



I thought: “Now where is the ego located? I know everybody thinks the seat of thinking is in the

brain, but how do they know that?” I knew already from reading things that it wasn’t so obvious to

people before a lot of psychological studies were made. The Greeks thought the seat of thinking

was in the liver, for instance. I wondered, “Is it possible that where the ego is located is learned by

children looking at people putting their hand to their head when they say, ‘Let me think’? Therefore

the idea that the ego is located up there, behind the eyes, might be conventional!” I figured that if I

could move my ego an inch to one side, I could move it further. This was the beginning of my


Feynmann didnt do his research properly.


”During the second half of the first millennium BC, the Ancient Greeks developed differing views on the function of the brain. It is said that it was the Pythagorean Alcmaeon of Croton (6th and 5th centuries BC) who first considered the brain to be the place where the mind was located. In the 4th century BC Hippocrates, believed the brain to be the seat of intelligence (based, among others before him, on Alcmaeon’s work). During the 4th century BC Aristotle thought that, while the heart was the seat of intelligence, the brain was a cooling mechanism for the blood. He reasoned that humans are more rational than the beasts because, among other reasons, they have a larger brain to cool their hot-bloodedness.[2]



Other kinds of errors are more characteristic of poor science. When I was at Cornell, I often

talked to the people in the psychology department. One of the students told me she wanted to do an

experiment that went something like this–it had been found by others that under certain

circumstances, X, rats did something, A. She was curious as to whether, if she changed the

circumstances to Y, they would still do A. So her proposal was to do the experiment under

circumstances Y and see if they still did A.


I explained to her that it was necessary first to repeat in her laboratory the experiment of the

other person–to do it under condition X to see if she could also get result A, and then change to Y

and see if A changed. Then she would know that the real difference was the thing she thought she

had under control.


She was very delighted with this new idea, and went to her professor. And his reply was, no, you

cannot do that, because the experiment has already been done and you would be wasting time. This

was in about 1947 or so, and it seems to have been the general policy then to not try to repeat

psychological experiments, but only to change the conditions and see what happens.


sadly, this is STILL the case!



So I have just one wish for you–the good luck to be somewhere where you are free to maintain

the kind of integrity I have described, and where you do not feel heed by a need to maintain your

position In the organization, or financial support, or so on, to lose your integrity. May you have that




Feynmann wud hav been sad to see the state of affairs of the modern publish or perish science, the lack of repetitions in various fields, the publication bias, the near impossibility of politically incorrect science.