statistically would actually make sense. Like, if you survey 10000 Women (which would still not be representative of the 170 million women in the US alone. For a 10% margin of error, the survey would need to collect data from 2.7 million women) and 75% say that they prefer men a certain way, you can say that women statistically prefer that trait.
That doesn't apply to all women, it just means that if you pick a random woman from a group, the chances that she prefers that trait over it's anti-trait are xx%.
Remember people, statistics are never about singular people, but about groups. The larger the group, the larger the sample must be to be representative.
Uhhh, can you please explain how you calculated that you need a 2.7 million sample size? that number is super-duper wrong. if you want to be 99% confident (way more confidence than statisticians need, most of the time) that your number is within a 5% margin of error, then of a population of 170 million (why you would include girl childrens' preference for men in a survey, I'm not sure) , you only need to randomly sample 666 people.
As always, if you think I'm wrong here, please explain how you calculated a 2.7 million sample size. I'd love to see the equation. Maybe you can teach me something new.
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u/Sea-Course-5171 May 04 '25
statistically would actually make sense. Like, if you survey 10000 Women (which would still not be representative of the 170 million women in the US alone. For a 10% margin of error, the survey would need to collect data from 2.7 million women) and 75% say that they prefer men a certain way, you can say that women statistically prefer that trait.
That doesn't apply to all women, it just means that if you pick a random woman from a group, the chances that she prefers that trait over it's anti-trait are xx%.
Remember people, statistics are never about singular people, but about groups. The larger the group, the larger the sample must be to be representative.