The way statistical sampling works, 1000 people in a population of 300,000,000 is actually good enough for most things. You can play around with numbers here to convince yourself, but at 95% confidence 1000 people will give an answer to within 3% of the true answer for the 300,000,000 population.
If the 300m people lived in the same area and you got a true random sample.
Sunsets at 9:09 today in Michigan
Sunsets at 8:04 today in California
Sunsets at 8:34 today in North Carolina
Sunsets at 7:57 today in Alabama
Sunsets at 7:38 today in Arizona (They are on standard time)
Sunsets at 7:13 today in Hawaii
Sunsets at 11:36 today in Alaska
Someone in Arizona might want the sun to set at 7:38. It's blazing hot all day.
Someone in Michigan might be fine with sunsetting at 8:08 with standard time.
Someone in Alabama might not want the sun to set at 6:57.
Someone in Hawaii probably doesn't want the sun to set at 6:13.
Even if you split up the 1000 people to equally represent all states, that's only 20 people per state.
I mean, yeah, 1000 people is enough assuming there's no sampling bias. But if you've got sampling bias, increasing the sampling size won't actually help you. The issue you're talking about is unrelated to how many people you talk to.
Your own suggestion of splitting up the respondents by state would itself introduce sampling bias, way over sampling low population states and way under sampling high population states. The survey was interested in the opinions of the nation as a whole, so arbitrary binning by states would be a big mistake. You want your sampling procedure to have equal change of returning a response from any random person in the nation. With a sample size of 1000, you're not going to have much random-induced bias for one location or another, aside from population density, which is fine because the survey is about USA people and not people in sub-USA locations.
The way statistical sampling works, 1000 people in a population of 300,000,000 is actually good enough for most things. You can play around with numbers here to convince yourself, but at 95% confidence 1000 people will give an answer to within 3% of the true answer for the 300,000,000 population.
If the 300m people lived in the same area and you got a true random sample.
Sunsets at 9:09 today in Michigan
Sunsets at 8:04 today in California
Sunsets at 8:34 today in North Carolina
Sunsets at 7:57 today in Alabama
Sunsets at 7:38 today in Arizona (They are on standard time)
Sunsets at 7:13 today in Hawaii
Sunsets at 11:36 today in Alaska
Someone in Arizona might want the sun to set at 7:38. It's blazing hot all day.
Someone in Michigan might be fine with sunsetting at 8:08 with standard time.
Someone in Alabama might not want the sun to set at 6:57.
Someone in Hawaii probably doesn't want the sun to set at 6:13.
Even if you split up the 1000 people to equally represent all states, that's only 20 people per state.
I mean, yeah, 1000 people is enough assuming there's no sampling bias. But if you've got sampling bias, increasing the sampling size won't actually help you. The issue you're talking about is unrelated to how many people you talk to.
Your own suggestion of splitting up the respondents by state would itself introduce sampling bias, way over sampling low population states and way under sampling high population states. The survey was interested in the opinions of the nation as a whole, so arbitrary binning by states would be a big mistake. You want your sampling procedure to have equal change of returning a response from any random person in the nation. With a sample size of 1000, you're not going to have much random-induced bias for one location or another, aside from population density, which is fine because the survey is about USA people and not people in sub-USA locations.