4 ms·

Lots of groups don't have 50/50 gender splits.

• theptip 9y ago
  It's odd to have to point this out in this forum; the tech community has female participation rates around 25%. That doesn't somehow make statistics about this community suspicious. Sure, if you didn't know that the base rate was skewed, you'd want to check your numbers the first time you made that measurement. But it's well-known (I suppose only by regular readers of SSC) that the SSC community is significantly above the population base rate on a whole host of metrics (self-reported IQ, transgender, % of males, autistic spectrum, etc). Edit: to clarify which point I was referring to
  • ikeboy 9y ago
    My point is that you shouldn't expect gender ratios to be 50/50 unless you're choosing at random, and therefore it's not suspicious
    • theptip 9y ago
      Sorry, should have made that more clear -- I agree with your point, your post's parent was the one I thought was strange.
  • mcguire 9y ago
    Professional (American) football also has participation rates varying greatly from 50%.
• Vinnl 9y ago
  In this case, I'd still be reluctant to make conclusions about birth-order effects if the gender split is not about 50/50.
  • monochromatic 9y ago
    The gender split is not expected to be 50/50 for this group.
    • ACow_Adonis 9y ago
      It may not be 50/50, but if you didn't select for gender in your survey design, it would be a "brave assumption" that gender isn't connected to your outcome/suggested mechanism in some way...
• optimuspaul 9y ago
  which is why you also need to take age into account. certain age groups will have different ratios, but the standard deviation for those age groups should cover for those variances.
  • ggggtez 9y ago
    This. Not taking into account actual age is completely wrong. Age of the respondent is going to show a skewed result (I bet none of your readers are 10 years old...). Does that mean the older sibling is "smarter"? No, it means young people are not your audience...
• ACow_Adonis 9y ago
  Yes, absolutely. That's why you also look at things like the distribution of birth day digits, which is something you expect to be standard in almost every data collection that didn't directly use that as an explicit selection factor. But regardless, back to gender. Lots of groups don't have 50/50 splits. But where they don't, it's elementary to discover that they don't. Plus it's a peice of information that is traditionally very accurately recorded: recording mistakes and misreporting compared to other variables in practice is almost traditionally the lowest of any variable amongst most populations of social data. How much they differ from 50/50, and why they differ is usually a big peice of the analytical puzzle. Someone else pointed out "well, professional footballers all men". But that's exactly my point, if you were generally interested in discovering things analytically, and you got a set of data sources from professional footballers, you'd discover all men, and quite rightly you'd investigate and find out "yeah, gender plays a real role in professional football". There's actual mechanisms and predictions you'd eventually find. But in this situation, where you're explaining birth order effects, we need to start hypothesising and looking for tests, falsifiable claims, and mechanisms of operation. Now, I don't know what mechanism is at work here, but you can bet that an almost 100 make presentation rate amongst a survey that didn't explicitly select for it is highly relevant to the puzzle. Why is it attracting these people? Are these representative males themselves? Are the females representative? Does the birth order effect only express itself in this community and these makes in these parts of the country/world/industry? What is its suggested mechanism of action that can be stated in a falsifiable way? If it was real, where else would you find it, and given our known distribution of makes in the population, how often and strong might you expect those effects to be?