The Nonlinear Library LW - 2023 Survey Results by Screwtape
Feb 16, 2024
01:31:31
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 2023 Survey Results, published by Screwtape on February 16, 2024 on LessWrong.
The Data
0. Population
There were 558 responses over 32 days. The spacing and timing of the responses had hills and valleys because of an experiment I was performing where I'd get the survey advertised in a different place, then watch how many new responses happened in the day or two after that.
Previous surveys have been run over the last decade or so.
2009: 166
2011: 1090
2012: 1195
2013: 1636
2014: 1503
2016: 3083
2017: "About 300"
2020: 61
2022: 186
2023: 558
Last year when I got a hundred and eighty six responses, I said that the cheerfully optimistic interpretation was "cool! I got about as many as Scott did on his first try!" This time I got around half of what Scott did on his second try. A thousand responses feels pretty firmly achievable.
This is also the tenth such survey that's been run. We missed a proper ten year anniversary in 2019, and in 2022 I was mostly focused on making the survey happen at all. Still, this is a cool milestone, and in celebration I'm going to be dipping into the datasets from previous years a lot. Unfortunately that doesn't mean I have ten surveys worth of data; bit rot and the rotation of census runners means I only have access to about half of these.
I'll talk about other surveys more later on. For the moment, let's talk about the basic breakdowns.
There's two main formats I'm going to present information in.
The simple one is where I give the answer, the number of people who gave that answer, and the percentage of the total respondents. For an example, let's use Previous LessWrong Surveys.
Previous LessWrong Surveys:
No: 349, 64.6%
Prefer not to answer: 25, 4.6%
Yes: 166, 30.7%
The other is where I have the mean and standard deviation. If you see a sequence of numbers like "30.1 + 8.9 (24, 28, 34) [n=186]" those numbers are "Mean + standard deviation (1st quartile, 2nd quartile, 3rd quartile) [n= number responding]." For an example, let's use Age.
Age: 30.5 + 9.2 (24, 29, 36) [n=552]
The mean is 30.5, the standard deviation is 9.2, the first quartile is 24, the second quartile (AKA the median) is 28, the third quartile is 34, and 552 people answered the question.
Got it? Good.
I. Demographics
Age: 30.5 + 9.2 (24, 29, 36) [n=552]
Then of course, there's times when it just made sense to me to treat a question differently. While the median age is useful, I also wanted to break it down into chunks so I could go by age group.
Under 20: 47, 8.5%
20 to 29: 236, 42.7%
30 to 39: 191, 34.6%
40 to 49: 53, 9.6%
50 to 59: 17, 3%
60 to 69: 8, 1.4%
That makes intuitive sense. We're mostly a community of twenty and thirty year olds. To make it a little visually clearer, here's a graph:
[I forgot to label my axes. The vertical axis is the number of respondents who gave that answer, the horizontal axis is how old they said they were.]
That's better, but I'm specifically curious about how the age of the community has changed over time. What happens if I pull the ages from all the censuses I have?
[I forgot to label my axes. The vertical axis is the number of respondents who gave that answer, the horizontal axis is how old they said they were. Each line is a different survey year.]
This mostly tells me that 2016 was a really good year for surveys. Fine. I'm going to come back to this later rather than get bogged down, but I'm not done with this.
The rest of the comparisons over time I saved for their own section.
Country:
United States of America: 274, 49.6%
Canada: 39, 7.1%
Germany:37, 6.7%
United Kingdom:34, 6.2%
Russia:20, 3.6%
France:17, 3.1%
Australia:16, 2.9%
India: 11, 2.0%
Finland,: 9, 1.6%
Poland: 9, 1.6%
Netherlands: 7, 1.3%
New Zealand: 7,1.3%
Norway: 7, 1.3%
Denmark: 5, 0.9%
Hungary: 4, 0.7%
Israel: 4, 0.7%
Other: 52, 9.4%
[I often rounded anyone at 3 respon...
The Data
0. Population
There were 558 responses over 32 days. The spacing and timing of the responses had hills and valleys because of an experiment I was performing where I'd get the survey advertised in a different place, then watch how many new responses happened in the day or two after that.
Previous surveys have been run over the last decade or so.
2009: 166
2011: 1090
2012: 1195
2013: 1636
2014: 1503
2016: 3083
2017: "About 300"
2020: 61
2022: 186
2023: 558
Last year when I got a hundred and eighty six responses, I said that the cheerfully optimistic interpretation was "cool! I got about as many as Scott did on his first try!" This time I got around half of what Scott did on his second try. A thousand responses feels pretty firmly achievable.
This is also the tenth such survey that's been run. We missed a proper ten year anniversary in 2019, and in 2022 I was mostly focused on making the survey happen at all. Still, this is a cool milestone, and in celebration I'm going to be dipping into the datasets from previous years a lot. Unfortunately that doesn't mean I have ten surveys worth of data; bit rot and the rotation of census runners means I only have access to about half of these.
I'll talk about other surveys more later on. For the moment, let's talk about the basic breakdowns.
There's two main formats I'm going to present information in.
The simple one is where I give the answer, the number of people who gave that answer, and the percentage of the total respondents. For an example, let's use Previous LessWrong Surveys.
Previous LessWrong Surveys:
No: 349, 64.6%
Prefer not to answer: 25, 4.6%
Yes: 166, 30.7%
The other is where I have the mean and standard deviation. If you see a sequence of numbers like "30.1 + 8.9 (24, 28, 34) [n=186]" those numbers are "Mean + standard deviation (1st quartile, 2nd quartile, 3rd quartile) [n= number responding]." For an example, let's use Age.
Age: 30.5 + 9.2 (24, 29, 36) [n=552]
The mean is 30.5, the standard deviation is 9.2, the first quartile is 24, the second quartile (AKA the median) is 28, the third quartile is 34, and 552 people answered the question.
Got it? Good.
I. Demographics
Age: 30.5 + 9.2 (24, 29, 36) [n=552]
Then of course, there's times when it just made sense to me to treat a question differently. While the median age is useful, I also wanted to break it down into chunks so I could go by age group.
Under 20: 47, 8.5%
20 to 29: 236, 42.7%
30 to 39: 191, 34.6%
40 to 49: 53, 9.6%
50 to 59: 17, 3%
60 to 69: 8, 1.4%
That makes intuitive sense. We're mostly a community of twenty and thirty year olds. To make it a little visually clearer, here's a graph:
[I forgot to label my axes. The vertical axis is the number of respondents who gave that answer, the horizontal axis is how old they said they were.]
That's better, but I'm specifically curious about how the age of the community has changed over time. What happens if I pull the ages from all the censuses I have?
[I forgot to label my axes. The vertical axis is the number of respondents who gave that answer, the horizontal axis is how old they said they were. Each line is a different survey year.]
This mostly tells me that 2016 was a really good year for surveys. Fine. I'm going to come back to this later rather than get bogged down, but I'm not done with this.
The rest of the comparisons over time I saved for their own section.
Country:
United States of America: 274, 49.6%
Canada: 39, 7.1%
Germany:37, 6.7%
United Kingdom:34, 6.2%
Russia:20, 3.6%
France:17, 3.1%
Australia:16, 2.9%
India: 11, 2.0%
Finland,: 9, 1.6%
Poland: 9, 1.6%
Netherlands: 7, 1.3%
New Zealand: 7,1.3%
Norway: 7, 1.3%
Denmark: 5, 0.9%
Hungary: 4, 0.7%
Israel: 4, 0.7%
Other: 52, 9.4%
[I often rounded anyone at 3 respon...