Running the results again on the SlateStarCodex Survey results of this year. EAs are less mentally ill than non-EAs, who are less mentally ill than respondents who identify as “sort of” EA. From this we should conclude that all three groups are all basically the same, because the difference is not significant at all.

The code used to find that out is, in R:

```
setwd(“my/directory”) ## directory in which the 2020ssc_public.xlsx file resides, downloadable from https://slatestarcodex.com/2020/01/20/ssc-survey-results-2020/
install.packages(“openxlsx”, dependencies = TRUE)
library(openxlsx)
D ← read.xlsx(“2020ssc_public.xlsx”)
mentally_ill_strict = c(“I have a formal diagnosis of this condition”)
mentally_ill_loose = c(“I have a formal diagnosis of this condition”, “I think I might have this condition, although I have never been formally diagnosed”)
m ← mentally_ill_strict
D$MentallyIll_1 = D$Depression %in% m | D$Anxiety %in% m | D$OCD %in% m | D$Eatingdisorder %in% m | D$PTSD %in% m | D$Alcoholism %in% m | D$Drugaddiction %in% m | D$Borderline %in% m | D$Bipolar %in% m
m ← mentally_ill_loose
D$MentallyIll_2 = D$Depression %in% m | D$Anxiety %in% m | D$OCD %in% m | D$Eatingdisorder %in% m | D$PTSD %in% m | D$Alcoholism %in% m | D$Drugaddiction %in% m | D$Borderline %in% m | D$Bipolar %in% m
summary(lm(D$MentallyIll_1 ~ D$EAID))
summary(glm(D$MentallyIll_1 ~ D$EAID, family = “binomial”)) ## This is a logistic regression
summary(lm(D$MentallyIll_1 ~ D$EAID))
summary(glm(D$MentallyIll_1 ~ D$EAID, family = “binomial”)) ## This is a logistic regression
```

And the results are

```
Call:
lm(formula = D$MentallyIll_1 ~ D$EAID)
Residuals:
Min 1Q Median 3Q Max
−0.5256 −0.5151 0.4744 0.4849 0.5325
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46753 0.04027 11.609 <2e-16 ***
D$EAIDNo 0.04757 0.04109 1.158 0.247
D$EAIDSorta 0.05811 0.04154 1.399 0.162
D$EAIDYes 0.03499 0.04328 0.808 0.419
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4998 on 7335 degrees of freedom
Multiple R-squared: 0.0004201, Adjusted R-squared: 1.124e-05
F-statistic: 1.027 on 3 and 7335 DF, p-value: 0.3792
> summary(glm(D$MentallyIll_1 ~ D$EAID, family = “binomial”)) ## This is a logistic regression
Call:
glm(formula = D$MentallyIll_1 ~ D$EAID, family = “binomial”)
Deviance Residuals:
Min 1Q Median 3Q Max
−1.221 −1.203 1.134 1.152 1.233
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) −0.1301 0.1615 −0.805 0.421
D$EAIDNo 0.1905 0.1648 1.156 0.248
D$EAIDSorta 0.2327 0.1666 1.397 0.162
D$EAIDYes 0.1401 0.1735 0.807 0.419
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 10167 on 7338 degrees of freedom
Residual deviance: 10164 on 7335 degrees of freedom
AIC: 10172
Number of Fisher Scoring iterations: 3
> summary(lm(D$MentallyIll_1 ~ D$EAID))
Call:
lm(formula = D$MentallyIll_1 ~ D$EAID)
Residuals:
Min 1Q Median 3Q Max
−0.5256 −0.5151 0.4744 0.4849 0.5325
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46753 0.04027 11.609 <2e-16 ***
D$EAIDNo 0.04757 0.04109 1.158 0.247
D$EAIDSorta 0.05811 0.04154 1.399 0.162
D$EAIDYes 0.03499 0.04328 0.808 0.419
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4998 on 7335 degrees of freedom
Multiple R-squared: 0.0004201, Adjusted R-squared: 1.124e-05
F-statistic: 1.027 on 3 and 7335 DF, p-value: 0.3792
> summary(glm(D$MentallyIll_1 ~ D$EAID, family = “binomial”)) ## This is a logistic regression
Call:
glm(formula = D$MentallyIll_1 ~ D$EAID, family = “binomial”)
Deviance Residuals:
Min 1Q Median 3Q Max
−1.221 −1.203 1.134 1.152 1.233
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) −0.1301 0.1615 −0.805 0.421
D$EAIDNo 0.1905 0.1648 1.156 0.248
D$EAIDSorta 0.2327 0.1666 1.397 0.162
D$EAIDYes 0.1401 0.1735 0.807 0.419
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 10167 on 7338 degrees of freedom
Residual deviance: 10164 on 7335 degrees of freedom
AIC: 10172
Number of Fisher Scoring iterations: 3
```

I’ve been following this with interest.

Re: Telecommunications performance, the red telephone might also be a a discontinuity in practical terms.

That is, even though faster systems existed, they hadn’t been implemented in the area of communications between the Soviet Union and the USA (pretty huge blindspot), but could be implemented more or less immediately, once both regimes actually bothered.

Also of interest to readers might be: some other discontinuities, one in passenger ship length and the other one on time needed to circumnavigate the Earth. AI impacts also has a couple of other discontinuities on their webpage, not mentioned/linked above:

Ship size (also pretty recently added)

From nuclear weapons

Altitude

...