Bigger, Stronger, and Faster — but Not Quicker?



There’s some controversial IQ research which suggests that reaction times have slowed and people are getting dumber, not smarter. Here’s Dr. James Thompson’s summary of the hypothesis:

We keep hearing that people are getting brighter, at least as measured by IQ tests. This improvement, called the Flynn Effect, suggests that each generation is brighter than the previous one. This might be due to improved living standards as reflected in better food, better health services, better schools and perhaps, according to some, because of the influence of the internet and computer games. In fact, these improvements in intelligence seem to have been going on for almost a century, and even extend to babies not in school. If this apparent improvement in intelligence is real we should all be much, much brighter than the Victorians.

Although IQ tests are good at picking out the brightest, they are not so good at providing a benchmark of performance. They can show you how you perform relative to people of your age, but because of cultural changes relating to the sorts of problems we have to solve, they are not designed to compare you across different decades with say, your grandparents.

Is there no way to measure changes in intelligence over time on some absolute scale using an instrument that does not change its properties? In the Special Issue on the Flynn Effect of the journal Intelligence Drs Michael Woodley (UK), Jan te Nijenhuis (the Netherlands) and Raegan Murphy (Ireland) have taken a novel approach in answering this question. It has long been known that simple reaction time is faster in brighter people. Reaction times are a reasonable predictor of general intelligence. These researchers have looked back at average reaction times since 1889 and their findings, based on a meta-analysis of 14 studies, are very sobering.

It seems that, far from speeding up, we are slowing down. We now take longer to solve this very simple reaction time “problem”.  This straightforward benchmark suggests that we are getting duller, not brighter. The loss is equivalent to about 14 IQ points since Victorian times.

So, we are duller than the Victorians on this unchanging measure of intelligence. Although our living standards have improved, our minds apparently have not. What has gone wrong? [“The Victorians Were Cleverer Than Us!” Psychological Comments, April 29, 2013]

Thompson discusses this and other relevant research in many posts, which you can find by searching his blog for Victorians and Woodley. I’m not going to venture my unqualified opinion of Woodley’s hypothesis, but I am going to offer some (perhaps) relevant analysis based on — you guessed it — baseball statistics.


It seems to me that if Woodley’s hypothesis has merit, it ought to be confirmed by the course of major-league batting averages over the decades. Other things being equal, quicker reaction times ought to produce higher batting averages. Of course, there’s a lot to hold equal, given the many changes in equipment, playing conditions, player conditioning, “style” of the game (e.g., greater emphasis on home runs), and other key variables over the course of more than a century.

Undaunted, I used the Play Index search tool at to obtain single-season batting statistics for “regular” American League (AL) players from 1901 through 2016. My definition of a regular player is one who had at least 3 plate appearances (PAs) per scheduled game in a season. That’s a minimum of 420 PAs in a season from 1901 through 1903, when the AL played a 140-game schedule; 462 PAs in the 154-game seasons from 1904 through 1960; and 486 PAs in the 162-game seasons from 1961 through 2016. I found 6,603 qualifying player-seasons, and a long string of batting statistics for each of them: the batter’s age, his batting average, his number of at-bats, his number of PAs, etc.

The raw record of batting averages looks like this, fitted with a 6th-order polynomial to trace the shifts over time:


That’s nice, you might say, but what accounts for the shifts? I considered 21 variables in an effort to account for the shifts, and ended up using 20 of the variables in a three-stage analysis.

In stage 1, I computed the residuals resulting from the application of the 6th-order polynomial. That is, I subtracted from the actual batting averages the estimates produced by the equation displayed in figure 1. For ease of reference, I call this first set of residuals the r1 residuals.

I began stage 2 by finding the correlations between each of the 21 candidate variables and the r1 residuals. I then estimated a regression equation with the r1 residuals as the dependent variable and the most highly correlated variable as the explanatory variable. I next found the correlations between the remaining 20 variables and the residuals of that regression equation. I introduced the most highly correlated variable into a new regression equation, as a second explanatory variable. I continued this process in the expectation that I would come across an explanatory variable that was statistically insignificant, at which point I would stop. But I ran through 16 explanatory variables without hitting a stopping point, and that exhausted the number of explanatory variables allowed by the regression function in Excel 2016.

The 16th regression on the r1 residuals left me with a set of residuals that I call the r2 residuals. In stage 3, I estimated a new equation with the r2 residuals as the dependent variable, following the same procedure that I used to obtain the 16-variable regression on the r1 residuals. In this case, I used 4 of the remaining explanatory variables; the 5th proved statistically insignificant.

I then combined the estimates obtained in the three stages to obtain the equation that’s discussed later, and at length. For now, I’ll focus on the apparent precision of the equation and its implications for the hypothesis that the general level of intelligence has declined with time.


Here’s how well the equation fits the data:


The 6th-order polynomial regression lines (black for actual, purple for estimated) are almost identical.

Here’s how the final estimates (vertical axis) correlate with the actual batting averages (horizontal axis):


I’ve never seen such a tight fit based on more than a few observations, and this one is based on 6,603 observations. I’m showing 6 decimal places in the trendline label so that you can see the 3 significant figures in the constant, which is practically zero.

Year (YR) enters as a significant variable in stage 3, with a coefficient of
-0.0000284 . (The 95-percent confidence interval is  -.0000214  to  -.0000355 ; the p-value is  3.40E-15 .) So, everything else being the same (a matter to which I’ll come), batting averages dropped by  .00327  between 1901 and 2016 ( -0.00327 =  -.0000284 x 115 ). (Note: It’s conventional to drop the 0 to the left of the decimal point in baseball statistics. And if you’re unfamiliar with baseball statistics, I can tell you that a difference of .00327 is taken seriously in baseball; many a batting championship race has been decided by a smaller margin.)

If the compound equation resulting from stages 1, 2, and 3 accounts satisfactorily for all changes affecting BA, the estimate of  -.00327  might be attributed to the slowing of batters’ reaction times. However, despite the statistical robustness of the coefficient on YR, it’s necessary to ask whether there are factors not properly accounted for that might point to the conclusion that reaction times have remained about the same or improved. To get at that question, I’ll present and discuss in the next section a table that summarizes the complete equation and all 20 of its explanatory variables. As you read and interpret the table, keep these points in mind:

The 6th-order polynomial (stage 1) is a filter. It captures the fluctuations over time that must be accounted for by the 20 “real” variables that are listed in the table (including YR) and discussed below the table. The “year” terms in the 6th-order polynomial are therefore irrelevant to the question of whether reaction times have slowed.

Every p-value in the stage-2 and stage-3 regression equations is smaller than  0.0001 , and most of them are far, far below that threshold.

The significance of the explanatory variables notwithstanding, the standard errors of the stage-1 and stage-2 equations are both about  .0027 . Therefore, the 95-percent confidence interval surrounding estimates of BA derived from those equations is plus or minus  .0053 . As discussed above, that’s not a small error in the context of baseball statistics. In fact, it’s enough to swamp the effect of YR.

As discussed below, many of the explanatory variables have intuitively incorrect signs and are highly correlated with each other. This casts doubt on the validity of the derived coefficients, including the coefficient on YR.

I don’t mean to say that reaction times have stayed the same or become faster. I simply mean that this analysis is inconclusive about the trend (if any) of reaction times — possibly because there is no trend, in one direction or the other.

The equation, taken as a whole, does an admirable job of accounting for changes in BA over the span of 115 years. But I can’t take any of its parts seriously.

It’s been great fun but it was just one of those things.


Table 1 gives the coefficients and maxima, minima, means, standard errors, and 95-percent confidence intervals around the coefficients of the explanatory variables. Statistical parameters and estimated values are expressed to three significant figures. For ease of comparison, I use decimal notation rather than scientific notation for the explanatory variables.


Next is table 2, which gives the cross-correlations among the explanatory variables (including the 21st variable that’s not in the equation). Positive correlations above 0.5 are highlighted in green; negative correlations below 0.5 are highlighted in yellow; statistically insignificant correlations are denoted by gray shading.

TABLE 2 (right-click to open a larger image in a new tab)

Here’s my explanation and interpretation of the instrumental variables:

Intercept (c) (shown in table 1)

This is the sum of the intercepts derived from the 6th-order polynomial fit and the stage-2 and stage-3 regression analyses.

On-base-plus-slugging percentage minus batting average (OPS – BA)

BA is embedded in both components of on-base-plus-slugging percentage (OPS). By subtracting BA from OPS, I partly decouple that relationship and obtain rough measure of a batter’s propensity to get on base (mainly) by walking, plus his propensity for hitting doubles, triples, and home runs. But see OBP – BA and SLB – BA, below.

Strikeouts per plate appearance (SO/PA)

The positive coefficient on SO/PA is counterintuitive. In any particular at-bat, striving to hit a home run is thought to reduce a batter’s ability to make contact with the ball. The positive coefficient therefore reflects the positive relationship between HR/PA and BA (see below), and the tendency of home-run hitters to strike out more often than other hitters.

On-base percentage minus batting average (OBP – BA)

The negative coefficient on this variable probably means that it’s compensating for the residual component of BA that lingers in OPS – BA. This variable and OPS – BA should be thought of as a complementary variable — one that’s meaningless without the other.

Home runs per plate appearance (HR/PA)

The positive coefficient on this variable seems to capture the positive relationship between HR and BA. For example, most of the great home-run hitters also compiled high batting averages. (Peruse this list.)

Integration (BLK)

I use this variable to approximate the effect of the influx of black players (including non-white Hispanics) since 1947. BLK measures only the fraction of AL teams that had at least one black player for each full season. It begins at 0.25 in 1948 (the Indians and Browns signed Larry Doby and Hank Thompson during the 1947 season) and rises to 1 in 1960, following the signing of Pumpsie Green by the Red Sox during the 1959 season. The positive coefficient on this variable is consistent with the hypothesis that segregation had prevented the use of players superior to many of the whites who occupied roster slots because of their color.

Deadball era (DBALL)

The so-called deadball era lasted from the beginning of major-league baseball in 1871 through 1919 (roughly). It was called the deadball era because the ball stayed in play for a long time (often for an entire game), so that it lost much of its resilience and became hard to see because it accumulated dirt and scuffs. Those difficulties (for batters) were compounded by the spitball, the use of which was officially curtailed beginning with the 1920 season. (See this and this.) Batting averages and the frequency of long hits (especially home runs) rose markedly after 1919. Given the secular trend shown in figure 1, it’s surprising to find a positive coefficient on DB, which is a dummy variable (value =1) assigned to all seasons from 1901-1919. So DB is probably picking up the net effect of other factors. It should be considered a complementary variable.

Performance-enhancing drugs (DRUG)

Their rampant use seems to have begun in the early 1990s and trailed off in the late 2000s. I assigned a dummy variable of 1 to all seasons from 1994 through 2007 in an effort to capture the effect of PEDs on BA. The resulting coefficient suggests that the effect was (on balance) negative, though slight. Players who used PEDs generally strove for long hits, which may have had the immediate effect of reducing their batting averages.

Slugging percentage minus batting average (SLG – BA)

I consider this variable to be a complement to OPS – BA and OBP – PA.

Number of major-league teams (MLTM)

The standard view is that expansion hurt the quality of play by diluting talent. However, expansion didn’t keep pace with population growth over the long run. (see POP/TM, below). In any event, MLTM should be considered another complementary variable.

Night baseball, that is, baseball played under lights (LITE)

It has long been thought that batting is more difficult under artificial lighting than in sunlight. This variable measures the fraction of AL teams equipped with lights, but it doesn’t measure the rise in night games as a fraction of all games. I know from observation that that fraction continued to rise even after all AL stadiums were equipped with lights. The positive coefficient on LITE suggests that it’s yet another complementary variable. It’s very highly correlated with BLK, for example.

Average age of AL pitchers (PAGE)

The r1 residuals rise with respect to PAGE rise until PAGE = 27.4 , then they begin to drop. This variable represents the difference between 27.4 and the average age of AL pitchers during a particular season. The coefficient is multiplied by 27.4 minus the average age of pitchers; that is, by a positive number for ages lower than 27.4, by zero for age 27.4, and by a negative number for ages above 27.4. The positive coefficient suggests that, other things being equal, pitchers younger than 27.4 give up hits at a lower rate than pitchers older than 27.4. I’m agnostic on the issue.

Complete games per AL team (CG/TM)

A higher rate of complete games should mean that starting pitchers stay in games longer, on average, and therefore give up more hits, on average. The positive coefficient seems to contradict that hypothesis. But there are other, related variables (P/TM and IP/P/G), so this one should be thought of as a complementary variable.

Number of pitchers per AL team (P/TM)

It, too, has a surprisingly positive coefficient. One would expect the use of more pitchers to cause BA to drop (see IP/P/G).

World War II (WW2)

A lot of the game’s best batters were in uniform in 1942-1945. That left regular positions open to older, weaker batters, some of whom wouldn’t otherwise have been regulars or even in the major leagues. The negative coefficient on this variable captures the war’s effect on hitting, which suffered despite the fact that a lot of the game’s best pitchers also served.

Bases on balls per plate appearance (BB/PA)

The negative coefficient on this variable suggests that walks are collected predominantly by above-average hitters, who are deprived of chances to hit safely. See, for example, the list of batters who collected the most career bases on balls. Anecdotally, during the many years when I regularly listened to and watched baseball games, announcers often spoke of the “intentional” unintentional walk and “pitching around” a batter. In both cases, a pitcher would aim for the outside edges of the plate, to avoid giving a batter a good pitch to hit. If that meant a walked batter and a chance to pitch to a weaker batter, so be it.

Innings pitched per AL pitcher per game (IP/P/G)

This variable reflects the long-term trend toward the use of more pitchers in a game, which means that batters more often face rested pitchers who come at them with a different delivery and repertoire of pitches than their predecessors. IP/P/G has dropped steadily over the decades, exerting a negative effect on BA. This is reflected in the positive coefficient on the variable, which means that BA rises with IP/P/G. But the effect is slight, and it’s prudent to treat this variable as a complement to CG/TM and P/TM.

AL fielding average (FA)

Fielding averages have risen generally since 1901, which was an especially bad year at .938. The climb from .949 in 1902 to .985 in 2016 was smooth and almost uninterrupted. How would that affect BA? Here’s an example: A line drive that in 1916 bounced off the edge of a fielder’s glove might have been counted as a hit or an error, and if it just missed the glove it would usually be counted as a hit. A century later the same line drive would almost always be caught in the much larger glove worn by a fielder in the same position. It therefore seems to me that the coefficient on this variable should be negative, that is, a higher FA should mean a lower BA. The positive coefficient points to a confounding factor (e.g., BLK).

Year (YR)

This is the crucial variable, and the value of its coefficient — given the inclusion of all the other variables — may say something about the IQ hypothesis. After taking into account the 19 other variables in this equation, the coefficient on YR is slightly negative, which suggests that batters have generally been getting a bit slower. But as discussed throughout this post, there’s much uncertainty about the validity of the equation and, therefore, about the validity of the coefficient on BA.

Maximum distance traveled by AL teams (TRV)

Does travel affect play? Probably, but the mode and speed of travel (airplane vs. train) probably also affects it. The slightly positive coefficient on this variable — which is highly correlated with YR, BLK, MLTM, and several others — is meaningless, except insofar as it combines with all the other variables to account for BA.

U.S. population in millions per major-league team (POP/TM)

POP/TM has been rising almost without pause, despite expansion, and is now at its peak value. The negative coefficient is therefore surprising, and probably reflects the strong correlation of POP/TM with BLK, and perhaps other variables.

Batter’s age (BAGE)

This is the 21st variable, which isn’t in the final equation. The r1 residuals don’t vary with BAGE until BAGE = 37 , whereupon the residuals begin to drop. Accordingly, this variable represents the difference between 37 and a player’s age during a particular season.

In sum, there’s no way of knowing whether the negative coefficient on YR is related to reaction time, the (probably) greater speed of today’s pitchers, the greater variety of pitches thrown by today’s pitchers,  or anything else that’s not adequately reflected by the 20 variables in the final equation. I rest my case and throw myself on the mercy of the court.

Not-So-Random Thoughts (XVII)


Links to the other posts in this occasional series may be found at “Favorite Posts,” just below the list of topics.

*     *     *

Victor Davis Hanson offers “The More Things Change, the More They Actually Don’t.” It echoes what I say in “The Fallacy of Human Progress.” Hanson opens with this:

In today’s technically sophisticated and globally connected world, we assume life has been completely reinvented. In truth, it has not changed all that much.
And he proceeds to illustrate his point (and mine).

*     *     *

Dr. James Thompson, and English psychologist, often blogs about intelligence. Here are some links from last year that I’ve been hoarding:

Intelligence: All That Matters” (a review of a book by Stuart Ritchie)

GCSE Genes” (commentary about research showing the strong relationship between genes and academic achievement)

GWAS Hits and Country IQ” (commentary about preliminary research into the alleles related to intelligence)

Also, from the International Journal of Epidemiology, comes “The Association between Intelligence and Lifespan Is Mostly Genetic.”

All of this is by way of reminding you of my many posts about intelligence, which are sprinkled throughout this list and this one.

*     *     *

How bad is it? This bad:

Thomas Lifson, “Mark Levin’s Plunder and Deceit

Arthur Milikh, “Alexis de Tocqueville Predicted the Tyranny of the Majority in Our Modern World

Steve McCann, “Obama and Neo-fascist America

Related reading: “Fascism, Pots, and Kettles,” by me, of course.

Adam Freedman’s book, A Less than Perfect Union: The Case for States’ Rights. States’ rights can be perfected by secession, and I make the legal case for it in “A Resolution of Secession.”

*     *     *

In a different vein, there’s Francis Menton’s series about anthropogenic global warming. The latest installment is “The Greatest Scientific Fraud of All Time — Part VIII.” For my take on the subject, start with “AGW in Austin?” and check out the readings and posts listed at the bottom.

Immigration and Intelligence


I haven’t written about intelligence since April 18, 2015 (here, third item). What’s on my mind now? This:

1. Immigrants to the U.S. are overwhelmingly poor and possibly (but not necessarily) below-average in intelligence.

2. The availability of immigrants seeking employment is a boon to entrepreneurs. Investments in capital (often modest ones such as lawn mowers and chain saws) can be turned into gainful employment for immigrants and profits for entrepreneurs.

3. The employment of immigrants is also a boon to American consumers, who are able to obtain some things more cheaply and some things that they might otherwise not be able to afford (e.g., fresh fruit, maid services, yard work).

4. Consumers should be indifferent about the origin of the labor that benefits them.

5. Taxpayers should care about the origin of labor only to the extent that immigration drives up the taxes because of state support for immigrants (e.g., schooling, medical care, welfare programs where citizenship isn’t a prerequisite).

6. Each taxpayer is also a consumer, and each taxpayer is therefore in a different position with respect to the net benefits (or costs) of immigration. But every consumer-citizen is likely to benefit to some degree because of immigration, though the benefit may not offset the rise in every consumer-citizen’s taxes.

7. Low-skilled Americans who have opted for the dole have no stake in the matter of immigration. If some low-skilled Americans lose jobs that they might otherwise have held, they aren’t “losers” any more than the wagon-makers who lost their jobs when automobiles come along. Voluntary economic change doesn’t have winners and losers — it takes arbitrary government interventions (e.g., minimum-wage laws) to create them.

8. Yes, government allows immigration, but the original intervention that created winners and losers is the one that restrained immigration. If it’s all right for a piece of fruit to move from Mexico to Texas, why isn’t it all right for a worker to move from Mexico to Texas? If it’s all right for a Californian to move to Texas, it is definitely all right for a Mexican to move to Texas.

9. So the only question is whether immigration imposes net costs on some consumers who are also taxpayers. And it’s an issue only because of government programs that allow immigrants to impose costs on taxpayers.

10. The real issue, for me, isn’t immigration, it’s government interventions that may encourage immigration (at a rate higher than the “natural” one) and subsidize immigrants. As usual, government is the problem, not the solution.

What does this have to do with intelligence? This post was spurred by a recent one at West Hunter by Gregory Cochran, “Our Dumb World.” Cochran’s post, combined with another one of his to which he links, can be read as follows:

  • There’s a strong link between the average IQ of a nation and its economic success. (True.)
  • Some things have skewed the relationship (e.g., the imposition of Communism), but the link is there nonetheless.
  • Mass migration from low-IQ countries (presumably Mexico and other Central American nations) to a country with a higher average IQ (e.g., the United States) will reduce the average IQ of the receiving country and therefore harm it economically.

I don’t buy it. For one thing, immigration — even immigration by low-skilled workers with (perhaps) below-average intelligence — can be a boon to the residents of the receiving country, as discussed above. For another thing:

Low-IQ immigrants do not reduce the productivity of high-IQ natives – any more than short immigrants reduce the height of tall natives. (See here for further discussion).

To repeat myself, the real issue is whether government action causes immigrants to impose burdens on natives that wouldn’t be imposed in the absence of government action. And to be clear, government action is any action that results in a rate of immigration which is higher or lower than would occur in the absence of that action (e.g., immigration quotas, implicit or explicit promises of government aid to indigent immigrants).

What about the political and cultural effects of massive immigration from south of the border? I am at the point of declaring that it doesn’t matter. The welfare state is so firmly entrenched in America that I really don’t expect it to be uprooted, except by non-electoral means. Mass culture is already so degenerate that it’s hard to see what could make it worse. And I have no reason to believe that, in general, Hispanics are more vulgar than American Anglos. (Just look at the prime-time TV lineup.) Those of us who prefer high culture can enjoy it without mingling with the hoi polloi.

I have been for years an opponent of illegal immigration. I am on the verge of changing my mind — something of which I am capable. My main reservation now has to do with the effect of mass immigration on crime, about which I can only offer conjectures.

Intelligence, Personality, Politics, and Happiness


This post, which I moved from my previous blog, Politics & Prosperity, is a collection and refinement of related posts at my earlier blog, Liberty Corner (with updated links). Each section of this post carries the same title as the original post at Liberty Corner. “IQ and Personality” is and has been, by far, the most popular of my Liberty Corner posts, so I give the eponymous section the place of honor in this post.

Web pages that link to this post usually consist of a discussion thread whose participants’ views of the post vary from “I told you so” to “that doesn’t square with me/my experience” or “MBTI is all wet because…”.  Those who take the former position tend to be persons of above-average intelligence whose MBTI types correlate well with high intelligence. Those who take the latter two positions tend to be persons who are defensive about their personality types, which do not correlate well with high intelligence. Such persons should take a deep breath and remember that high intelligence (of the abstract-reasoning-book-learning kind measured by IQ tests) is widely distributed throughout the population. As I say below, ” I am not claiming that a small subset of MBTI types accounts for all high-IQ persons, nor am I claiming that a small subset of MBTI types is populated entirely by high-IQ persons.” All I am saying is that the bits of evidence which I have compiled suggest that high intelligence is more likely — but far from exclusively — to be found among persons with certain MBTI types.

The correlations between intelligence, political leanings, and happiness are admittedly more tenuous. But they are plausible.

Leftists who proclaim themselves to be more intelligent than persons of the right do so, in my observation, as a way of reassuring themselves of the superiority of their views. They have no legitimate basis for claiming that the ranks of highly intelligent persons are dominated by the left. Leftist “intellectuals” in academia, journalism, the “arts,” and other traditional haunts of leftism are prominent because they are vocal. But they comprise a small minority of the population and should not be mistaken for typical leftists, who seem mainly to populate the ranks of the civil service, labor unions, the teaching “profession,” and the unemployed. (It is worth noting that public-school teachers, on the whole, are notoriously dumber than most other college graduates.)

Again, I am talking about general relationships, to which there are many exceptions. If you happen to be an exception, don’t take this post personally. You’re probably an exceptional person.


Some years ago I came across some statistics about the personality traits of high-IQ persons (those who are in the top 2 percent of the population).* The statistics pertain to a widely used personality test called the Myers-Briggs Type Indicator (MBTI), which I have taken twice. In the MBTI there are four pairs of complementary personality traits, called preferences: Extraverted/Introverted, Sensing/iNtuitive, Thinking/Feeling, and Judging/Perceiving. Thus, there are 16 possible personality types in the MBTI: ESTJ, ENTJ, ESFJ, ESFP, and so on. (For an introduction to MBTI, summaries of types, criticisms of MBTI, and links to other sources, see this article at Wikipedia. A straightforward description of the theory of MBTI and the personality traits can be found here. Detailed descriptions of the 16 types are given here.)

In summary, here is what the statistics indicate about the correlation between personality traits and IQ:

  • Other personality traits being the same, an iNtuitive person (one who grasps patterns and seeks possibilities) is 25 times more likely to have a high IQ than a Sensing person (one who focuses on sensory details and the here-and-now).
  • Again, other traits being the same, an Introverted person is 2.6 times more likely to have a high IQ than one who is Extraverted; a Thinking (logic-oriented) person is 4.5 times more likely to have a high IQ than a Feeling (people-oriented) person; and a Judging person (one who seeks closure) is 1.6 times as likely to have a high IQ than a Perceiving person (one who likes to keep his options open).
  • Moreover, if you encounter an INTJ, there is a 22% probability that his IQ places him in the top 2 percent of the population. (Disclosure: I am an INTJ.) Next are INTP, at 14%; ENTJ, 8%; ENTP, 5%; and INFJ, 5%. (The next highest type is the INFP at 3%.) The  five types (INTJ, INTP, ENTJ, ENTP, and INFJ) account for 78% of the high-IQ population but only 15% of the total population.**
  • Four of the five most-intelligent types are NTs, as one would expect, given the probabilities cited above. Those same probabilities lead to the dominance of INTJs and INTPs, which account for 49% of the Mensa membership but only 5% of the general population.**
  • Persons with the S preference bring up the rear, when it comes to taking IQ tests.**

A person who encountered this post when it was at Liberty Corner claims that “one would expect to see the whole spectrum of intelligences within each personality type.” Well, one does see just that, but high intelligence is skewed toward the five types listed above. I am not claiming that a small subset of MBTI types accounts for all high-IQ persons, nor am I claiming that a small subset of MBTI types is populated entirely by high-IQ persons.

I acknowledge reservations about MBTI, such as those discussed in the Wikipedia article. An inherent shortcoming of psychological tests (as opposed to intelligence tests) is that they rely on subjective responses (e.g., my favorite color might be black today and blue tomorrow). But I do not accept this criticism:

[S]ome researchers expected that scores would show a bimodal distribution with peaks near the ends of the scales, but found that scores on the individual subscales were actually distributed in a centrally peaked manner similar to a normal distribution. A cut-off exists at the center of the subscale such that a score on one side is classified as one type, and a score on the other side as the opposite type. This fails to support the concept of type: the norm is for people to lie near the middle of the subscale.[6][7][8][33][42]

Why was “it was expected” that scores on a subscale (E/I, S/N, T/F, J/P) would show a bimodal distribution? How often does one encounter a person who is at the extreme end of any subscale? Not often, I wager, except in places where such extremes are likely to be clustered (e.g., Extraverts in acting classes, Introverts in monasteries). The cut-off at the center of each subscale is arbitrary; it simply affords a shorthand characterization of a person’s dominant traits. But anyone who takes an MBTI (or equivalent instrument) is given his scores on each of the subscales, so that he knows the strength (or weakness) of his tendencies.

Regarding other points of criticism: It is possible, of course, that a person who is familiar with MBTI tends to see in others the characteristics of their known MBTI types (i.e., confirmation bias). But has that tendency been confirmed by rigorous testing? Such testing would examine the contrary case, that is, the ability of a person to predict the type of a person whom he knows well (e.g., a co-worker or relative). The supposed vagueness of the descriptions of the 16 types arises from the complexity of human personality; but there are differences among the descriptions, just as there are differences among individuals. According to a footnote to an earlier version of the Wikipedia article about MBTI, half of the persons who take the MBTI are able to guess their types before taking it. Does that invalidate MBTI or does it point to a more likely phenomenon, namely, that introspection is a personality-related trait, one that is more common among Introverts than Extraverts? A good MBTI instrument cuts through self-deception and self-flattery by asking the same set of questions in many different ways, and in ways that do not make any particular answer seem like the “right” one.

My considerable exposure to high-IQ scientists in 30 years of working with them is suggestive. Most of them seemed to exhibit the traits of INTJs and INTPs. And those who took an MBTI test were found to be INTJs and INTPs.


It is hard to find clear, concise analyses of the relationship between IQ and political leanings. I offer the following in evidence that very high-IQ individuals lean strongly toward libertarian positions.

The Triple Nine Society (TNS) limits its membership to persons with IQs in the top 0.1% of the population. In an undated survey (probably conducted in 2000, given the questions about the perceived intelligence of certain presidential candidates), members of TNS gave their views on several topics (in addition to speculating about the candidates’ intelligence): subsidies, taxation, civil regulation, business regulation, health care, regulation of genetic engineering, data privacy, death penalty, and use of military force.

The results speak for themselves. Those members of TNS who took the survey clearly have strong (if not unanimous) libertarian leanings.


I count libertarians as part of the right because libertarians’ anti-statist views are aligned with the views of the traditional (small-government) conservatives who are usually Republicans. Having said that, the results reported in “IQ and Politics” lead me to suspect that the right is smarter than the left, left-wing propaganda to the contrary notwithstanding. There is additional evidence for my view.

A site called Personality Page offers some data about personality type and political affiliation. The sample is not representative of the population as a whole; the average age of respondents is 25, and introverted personalities are overrepresented (as you might expect for a test that is apparently self-administered through a web site). On the other hand, the results are probably unbiased with respect to intelligence because the data about personality type were not collected as part of a study that attempts to relate political views and intelligence, and there is nothing on the site to indicate a left-wing bias. (Psychologists, who tend toward leftism, have a knack for making conservatives look bad, as discussed here, here, and here. If there is a strong association between political views and intelligence, it is found among so-called intellectuals, where the herd mentality reigns supreme.)

The data provided by Personality Page are based on the responses of 1,222 individuals who took a 60-question personality test that determined their MBTI types (see “IQ and Personality”). The test takers were asked to state their political preferences, given these choices: Democrat, Republican, middle of the road, liberal, conservative, libertarian, not political, and other. Political self-labelling is an exercise in subjectivity. Nevertheless, individuals who call themselves Democrats or liberals (the left) are almost certainly distinct, politically, from individuals who call themselves Republicans, conservatives, or libertarians (the right).

Now, to the money question: Given the distribution of personality types on the left and right, which distribution is more likely to produce members of Mensa? The answer: Those who self-identify as persons of the right are 15% more likely to qualify for membership in Mensa than those who self-identify as persons of the left. This result is plausible because it is consistent with the pronounced anti-government tendencies of the very-high-IQ members of the Triple Nine Society (see “IQ and Politics”).


That statement follows from research by the Pew Research Center (“Are We Happy Yet?” February 13, 2006) and Gallup (“Republicans Report Much Better Health Than Others,” November 30, 2007).

Pew reports:

Some 45% of all Republicans report being very happy, compared with just 30% of Democrats and 29% of independents. This finding has also been around a long time; Republicans have been happier than Democrats every year since the General Social Survey began taking its measurements in 1972….

Of course, there’s a more obvious explanation for the Republicans’ happiness edge. Republicans tend to have more money than Democrats, and — as we’ve already discovered — people who have more money tend to be happier.

But even this explanation only goes so far. If one controls for household income, Republicans still hold a significant edge: that is, poor Republicans are happier than poor Democrats; middle-income Republicans are happier than middle-income Democrats, and rich Republicans are happier than rich Democrats.

Gallup adds this:

Republicans are significantly more likely to report excellent mental health than are independents or Democrats among those making less than $50,000 a year, and among those making at least $50,000 a year. Republicans are also more likely than independents and Democrats to report excellent mental health within all four categories of educational attainment.

There is a lot more in both sources. Read them for yourself.

Why would Republicans be happier than Democrats? Here’s my thought, Republicans tend to be conservative or libertarian (at least with respect to minimizing government’s role in economic affairs). I refer you to a post in which I discussed Thomas Sowell’s A Conflict of Visions:

He posits two opposing visions: the unconstrained vision (I would call it the idealistic vision) and the constrained vision (which I would call the realistic vision). As Sowell explains, at the end of chapter 2:

The dichotomy between constrained and unconstrained visions is based on whether or not inherent limitations of man are among the key elements included in each vision…. These different ways of conceiving man and the world lead not merely to different conclusions but to sharply divergent, often diametrically opposed, conclusions on issues ranging from justice to war.

Idealists (“liberals”) are bound to be less happy than realists (conservatives and libertarians) because idealists’ expectations about human accomplishments (aided by government) are higher than those of realists, and so idealists are doomed to disappointment.

All of this is consistent with findings reported by law professor James Lindgren:

[C]ompared to anti-redistributionists, strong redistributionists have about two to three times higher odds of reporting that in the prior seven days they were angry, mad at someone, outraged, sad, lonely, and had trouble shaking the blues. Similarly, anti-redistributionists had about two to four times higher odds of reporting being happy or at ease. Not only do redistributionists report more anger, but they report that their anger lasts longer. When asked about the last time they were angry, strong redistributionists were more than twice as likely as strong opponents of leveling to admit that they responded to their anger by plotting revenge. Last, both redistributionists and anti-capitalists expressed lower overall happiness, less happy marriages, and lower satisfaction with their financial situations and with their jobs or housework. (From the abstract of Northwestern Law and Economics Research Paper 06-29, “What Drives Views on Government Redistribution and Anti-Capitalism: Envy or a Desire for Social Dominance?,” March 15, 2011.)


If you are very intelligent — with an IQ that puts you in the top 2% of the population — you are most likely to be an INTJ, INTP, ENTJ, ENTP, or INFJ, in that order. Your politics will lean heavily toward libertarianism or small-government conservatism. You probably vote Republican most of the time because, even if you are not a card-carrying Republican, you are a staunch anti-Democrat. And you are a happy person because your expectations are not constantly defeated by reality.

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* I apologize for not having documented the source of the statistics that I cite here. I dimly recall finding them on or via the website of American Mensa, but I am not certain of that. And I can no longer find the source by searching the web. I did transcribe the statistics to a spreadsheet, which I still have. So, the numbers are real, even if their source is now lost to me.

** Estimates of the distribution of  MBTI types  in the U.S. population are given in two tables on page 4 of “Estimated Frequencies of the Types in the United States Population,” published by the Center for Applications of Psychological Type. One table gives estimates of the distribution of the population by preference (E, I, N, S, etc.). The other table give estimates of the distribution of the population among all 16 MBTI types. The statistics for members of Mensa were broken down by preferences, not by types; therefore I had to use the values for preferences to estimate the frequencies of the 16 types among members of Mensa. For consistency, I used the distribution of the preferences among the U.S. population to estimate the frequencies of the 16 types among the population, rather than use the frequencies provided for each type. For example, the fraction of the population that is INTJ comes to 0.029 (2.9%) when the values for I (0.507), N (0.267), T (0.402), and J (0.541) are multiplied. But the detailed table has INTJs as 2.1% of the population. In sum, there are discrepancies between the computed and given values of the 16 types in the population. The most striking discrepancy is for the INFJ type. When estimated from the frequencies of the four preferences, INFJs are 4.4% of the population; the table of values for all 16 types gives the percentage of INFJs as 1.5%.

Using the distribution given for the 16 types leads to somewhat different results:

  • There is a 31% probability that an INTJ’s his IQ places him in the top 2 percent of the population. Next are INFJ, at 14%; ENTJ, 13%; and INTP, 10%. (The next highest type is the ENTP at 4%.) The  four types (INTJ, INFJ, ENTJ, AND INTP) account for 72% of the high-IQ population but only 9% of the total population. The top five types (including ENTPs) account for 78% of the high-IQ population but only 12% of the total population.
  • Four of the five most-intelligent types are NTs, as one would expect, given the probabilities cited earlier. But, in terms of the likelihood of having an IQ, this method moves INFJs into second place, a percentage point ahead of ENTJs.
  • In any event, the same five types dominate, and all five types have a preference for iNtuitive thinking.
  • As before, persons with the S preference generally lag their peers when it comes to IQ tests.

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Related posts:
Intelligence as a Dirty Word
Intelligence and Intuition