… However, I think p < 0.0005 would still be significant even with the data mining aspect taken in to account.

This is the error that made that one study show that prayer had a positive effect on medical outcomes, they were using something like 20 different measures of outcome, and come up with a weakly significant outcome in 1 of them, which they trumpeted to the news, but they had forgotten to adjust for the fact that if you use 20 different measurements of outcomes, the chance that one of them will have an outcome that only has a 1 in 20 likelihood (p = 0.05) is actually quite high. Total tangent, but that is what always reminds me of that rule (or vice versa).

I don’t read statistics as sadistics, but I do read discipline as de-splining. A typo that would always bring cries of “safeword! safeword!” from the engineers.

With the data in the right order, and the population data from the same Census 2000 source, I show an an EC shift of 6.52 votes, and an r-squared of 0.09. Still not huge, but bigger. I don’t know where the discrepancy is.

The population numbers in the table at the top of this post don’t match the census numbers. Your EV numbers are right though. Using the census numbers from Rachel’s link, and recalculating the people/EV based on those numbers, I reran my calculations again, being careful that I wasn’t misaligning states in the merger of the data sets (I wasn’t, but it was the first source of error I thought of). Using 2000 population numbers, the r-squared goes up a little bit.

rsquared F-test p
0.0694 3.5821 0.0644 %whites
0.2252 13.9509 0.0005 %blacks
0.1766 10.2984 0.0024 %indian
0.0014 0.0684 0.7948 %asian (I lose my bet from earlier up)
0.0308 1.5264 0.2227 %hawaiian
0.0628 3.2148 0.0793 %other race
0.0184 0.8994 0.3477 %biracial
0.0597 3.0475 0.0873 %hispanic
0.1215 6.6416 0.0131 %white not hispanic

Also, if you look at % of pop black or % of pop American Indians and Alaskan Natives (rather than % of pop white), r-squared goes up, and p goes down (for black % of pop regressed on EV, I get p of 0.0012 and r-squared of 0.19). White (non-latino) % of pop regressed on EV still gives rsquared of 0.12 and p of 0.013.

Using those numbers for % white, the EC disparity shrinks, to 2.86 net EC votes that would shift hands. R-squared shrinks to 0.04.

The trouble is that social science data is almost always very noisy. It has all sorts of non-independent variables, data biases, data sensitive to minor definitional changes (like whether multi-racial is included or not) and small data sets.

Honestly, the remarkable think about the balance of power between the states is not that the electoral college and Senate have screwed it up, but, the fortunate coincidence that the partisan balance in the Senate and among the states in Presidential election has rarely differed dramatically from the partisan balance in the House, something that nothing in the system makes a particularly necessary or likely result.

“Census 2000 showed that the United States population on April 1,2000 was 281.4 million. Of the total,
216.9 million, or 77.1 percent, reported White. This number includes 211.5 million people, or 75.1 percent,
who reported only White in addition to 5.5 million people, or 1.9 percent, who reported White as well as one
or more other races. Census 2000 asked separate questions on race and Hispanic or Latino origin. Hispanics
who reported their race as White, either alone or in combination with one or more other races, are included in the numbers for Whites.”

Robert’s data is skewed because it includes Latinos who identify as white. The Census uses two questions to measure race/spanish origin. Historically, most Latinos have identified their race as White on Census forms, although more recently many are checking “other.” This alters the data somewhat, but not drastically. You should use the data from the final column.

So I understand.