It is still a sample, which is therefore subject to a margin of error. Unless you think this data accounts for all CPR given anywhere to anyone, ever.
For example, if they’d only sampled one man and one woman, and the man reported receiving CPR and the woman reported not, the “study” would show 100% of men and 0% of women receive CPR. Staggering “real-life numbers”!
I’m aware. My point is that “real life numbers” still have margins of error. The person to whom I’m responding implied that “real life numbers” aren’t subject to a margin of error.
To add to your point with a very clear example: If I did a study to check the average age of people in a country where I mainly checked the age of people living in retirement homes, the margin of error would be huge even if I got the age from hundreds of thousands of people.
In more general terms: there can be systemic errors due to methodology that no increasing of the number of samples will remove.
Thank you, that’s an important point to make. There’s this belief that big samples are more relevant than small samples, but that is far from the truth.
The methodology is what’s vital to the data’s significance.
This isn’t a pole. This isn’t self reported numbers. Those are real life numbers
It is still a sample, which is therefore subject to a margin of error. Unless you think this data accounts for all CPR given anywhere to anyone, ever.
For example, if they’d only sampled one man and one woman, and the man reported receiving CPR and the woman reported not, the “study” would show 100% of men and 0% of women receive CPR. Staggering “real-life numbers”!
All of science is just a sample. Population trends can be observed in smaller subsets.
I’m aware. My point is that “real life numbers” still have margins of error. The person to whom I’m responding implied that “real life numbers” aren’t subject to a margin of error.
Pretty much all data has margins of error, including “real life data”. The margin of error just often doesn’t matter.
But is it a poll?
It doesn’t matter, a margin of error exists regardless of the data source.
To add to your point with a very clear example: If I did a study to check the average age of people in a country where I mainly checked the age of people living in retirement homes, the margin of error would be huge even if I got the age from hundreds of thousands of people.
In more general terms: there can be systemic errors due to methodology that no increasing of the number of samples will remove.
Thank you, that’s an important point to make. There’s this belief that big samples are more relevant than small samples, but that is far from the truth.
The methodology is what’s vital to the data’s significance.