It must be known that a p-value, or any other statistics based on the Chi-Square, is not a useful number. It has two components : sample size and effect size. Its ability to detect a non-zero difference increases when either sample size or effect size increases. If only sample size increases, even with the other left constant, the statistics become inflated. There is also a problem with the assumption. If it is about the detection of “non-zero” difference, it is of no use if the magnitude, i.e., effect size, is of no importance. I will provide several examples of the dangerosity of the significance tests.
It goes without saying that multiple regression is one of most popular and applied statistical methods. Thus, it would be odd if most practitioners among scientists and researchers do not understand and misapply it. And yet, this provocative conclusion seems most likely.
Because a simple bivariate correlation does not disentangle confounding effects, the multiple regression is said to be preferred. The technique attempts to evaluate the strength of an independent (predictor) variable in the prediction of an outcome (dependent) variable, when controlling, i.e., holding constant, every other variables entered (included) as independent variables into the regression model, either progressively step by step or altogether at the same time. The rationale is to get the effect of an independent variable that only belongs to it. But this is a fallacy.
Burma, also known as Myanmar, has a population of over 60 million, and is the world’s 24th most populous nation. With an authoritarian, military-controlled government, it is also one of the poorest and most dysfunctional places on earth—you will find it nestled together with mostly African countries at the back of most human development rankings.
Richard Lynn’s international dataset does not yet have a study for Burma. IQ and the Wealth of Nations (2002, p. 74) makes an estimate of 86 by averaging together IQ from neighboring India (81) and Thailand (91). IQ and Global Inequality (2006, p. 59) bumps up India’s IQ to 82, which changes the Burma estimate to 87. The latest version of the dataset (Lynn & Vanhanen, 2012, p. 26) assigns a lower IQ to Thailand (88), which means that Lynn’s most recent estimate for Burma is 85.
I was able to locate one published intelligence study for Burma. The results are surprising, but the research contains no obvious flaws. Intellectual potential in Southeast Asia is an issue filled with contradiction and uncertainty.
The American Virgin Islands are a territorial possession of the United States. According to the 2010 census, it has a population of 106,405 and an ethnic composition that is 76% black and 15.6% white. Almost all of the inhabitants live on three main islands: St. Croix, St. John, and St. Thomas. Virgin Islanders, much like Puerto Ricans, are United States citizens, but there has not been a similar push for U.S. statehood in this small territory.
Here I discuss several studies that have looked at the intelligence and academic skills of Virgin Islanders.
Multivariate genetic analyses and simple correlational analyses have been conducted to evaluate the extent to which the general factor (g) of intelligence is differentially heritable, compared to, for example, group factors. A positive correlation would be supportive of Jensen’s view, notably advanced in The g Factor (1998), of the heritable g. This can be interpreted to say that what makes people being good at all tests has more genetic component than what make people being good at one specific test. On the other hand, if environmental effects are smaller at the g level, it would mean that what make people being good at all tests has less environmental component than what make people good at one specific test. Similarly, if heritability is large at the g level and environment is small at the g level, then g differences between persons are largely genetic, not environmental (Plomin, 2003, p. 186).
The present article is a review of the studies published so far and can be seen as a complement to my article on the genetics of intelligence. Brody (2007) and Deary (2006) have already reviewed a large part of the existing studies. But some features need to be highlighted. The article can be subjected to modifications if I happen to read some more studies not listed here (I prefer not to publish a new article each time I read a new research paper). Shortly, there seems to be some proof of differential heritabilities, higher for g. But it’s not overwhelming.
Fuerst, J., Kirkegaard, E. O. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.
We obtained data from Denmark for the largest 71 immigrant groups by country of origin. We show that three important socialeconomic variables are highly predictable from the Islam rate, IQ, GDP and height of the countries of origin. We further show that there is a general immigrant socioeconomic factor and that country of origin national IQs, Islamic rates, and GDP strongly predict immigrant general socioeconomic scores.
Fuerst, J., Kirkegaard, E. O. (2014). Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman. Open Differential Psychology.
We discuss the global hereditarian hypothesis of race differences in g and test it on data from the NLSF. We find that migrants country of origin’s IQ predicts GPA and SAT/ACT.