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Statistics is as much (if not more) about careful thought as it is about applying standard tests. What sampling biases do you think might be present in this sa

by mrow84 11y ago

Statistics is as much (if not more) about careful thought as it is about applying standard tests. What sampling biases do you think might be present in this sampling that invalidate the stated conclusions? For example, for a conclusion about gender differences to be exaggerated by the data there would have to be systematic biases to sampling women with lower salaries, and men with higher salaries, in the same position. Does this seem likely?

walshemj 11y ago
Ah the want do you want 2+2 to equal boss :-)
cperciva 11y ago
For a conclusion about gender differences to be exaggerated by the data there would have to be systematic biases to sampling women with lower salaries, and men with higher salaries, in the same position. Does this seem likely? For a self-selected sample, absolutely. Self-worth is far more tied to salary in males than in females. (I don't think this is innate; I think it's a result of social conditioning which tells males that their role in society is to be breadwinners.) But you don't need to be able to point to a specific source of bias to say that self-selected samples are problematic. It's up to the person who intends to use the statistics to show that they were either gathered reliably or carefully examined to remove biases.
- mrow84 11y ago
  I don't understand the link you're drawing between self-worth and willingness to participate in this spreadsheet exercise, but I suppose it is beside the point. I certainly agree that if someone is trying to make a statistical claim then they should be the ones to do the due diligence on the data, and on their analyses, to show that they stand up - I would certainly expect this in a more formal publication. What I am disputing is that one can dismiss the conclusions simply due to the lack of these checks, without considering what the data might tell you. Nearly always such dismissive arguments strike me as a disagreement with the conclusions, rather than a claim about failings in the data - a more convincing argument would present a reason why the data are flawed, such as the one you have given in this response (even if I don't understand it).

by mrow84 11y ago

walshemj 11y ago

Ah the want do you want 2+2 to equal boss :-)

cperciva 11y ago

For a conclusion about gender differences to be exaggerated by the data there would have to be systematic biases to sampling women with lower salaries, and men with higher salaries, in the same position. Does this seem likely? For a self-selected sample, absolutely. Self-worth is far more tied to salary in males than in females. (I don't think this is innate; I think it's a result of social conditioning which tells males that their role in society is to be breadwinners.) But you don't need to be able to point to a specific source of bias to say that self-selected samples are problematic. It's up to the person who intends to use the statistics to show that they were either gathered reliably or carefully examined to remove biases.

mrow84 11y ago
I don't understand the link you're drawing between self-worth and willingness to participate in this spreadsheet exercise, but I suppose it is beside the point. I certainly agree that if someone is trying to make a statistical claim then they should be the ones to do the due diligence on the data, and on their analyses, to show that they stand up - I would certainly expect this in a more formal publication. What I am disputing is that one can dismiss the conclusions simply due to the lack of these checks, without considering what the data might tell you. Nearly always such dismissive arguments strike me as a disagreement with the conclusions, rather than a claim about failings in the data - a more convincing argument would present a reason why the data are flawed, such as the one you have given in this response (even if I don't understand it).