I briefly covered the NxALT error in an earlier essay on “AWALT” (all women are like that), but as it seems to be catching on in various domains relevant to, or sphere adjacent, it is time for a dedicated essay. Whenever I view characteristics of a population, I tend to make the initial assumption that it follows a normal distribution similar to the bell curve depicted in this essay.

Such a distribution is characterized by the fact that the values cluster around the mean, and the further away one gets from the mean, the smaller the population will be. For instance, in regards to IQ, 68% of the population are within 1 standard deviation either above or below the mean, meaning that they have an IQ in the range 85 – 115. 95% of the population are within 2 standard deviations either above or below the mean, meaning an IQ in the range 70 – 130. When one enters the outliers, meaning an IQ either below below 70 or above 130, this totals a mere 4.2% of the population. The extreme outliers, those people with an IQ either above 145 or below 55, are a mere 2% of the total population.

The normal distribution is present in many observations of human traits, height, weight and IQ being among them. In Gendernomics I argue that sexual market value should be viewed as a normal distribution, as this would be the distribution that ensured the maximal chance of “pairing off” when one takes hypergamy and the female pareto attraction into account. If all men are 10s, then it becomes impossible for hypergamy to select the highest value males, likewise if all women are 10s, then it becomes impossible for women to ensure that they have optimized hypergamy.

To summarize, in a normal distribution the majority of observations are within 1 – 2 standard deviations of the mean value, and the further one gets away from the mean the lower the amount of observations one makes. Thus it follows, that the probability of making an observation that is within 1 – 2 standard deviations of the mean is much higher than to observe an outlier. Continue reading