> I can think of plenty of cases where gender does make a difference. =
But on many
> traits, the differences seem overrated. For example, height. Males are =
on average
> slightly taller than females. But if I were to tell you "You will =
shortly meet two
> people. One is a native Thai adult male, raised in Thailand. The other =
is a native
> Masai woman, raised in East Africa". You would lose money (on average) =
betting that the
> male will be taller. Other factors swamp the gender bias.
When using statistics, you can go only one way:analyse a small part of a = group and apply the results to the whole group. You cannot guess from a = group to an individual. So, racial differences cannot be judeged based = on a one-on-one scheme. You can only take averages between large groups. = This is no problem with physical attributes, which can be determined = without problem. But any mentaltrstposes some problems. Many = psychologial test systems are "localized", developped to be used only on = specific groups with certain caracteristics. IMO there does not exist = any objective method of determining intelligence or other mental = capabilities.
You could guess from historic and ethnic development on the advantages = and disadvantages of civilizations. You couls say that, based on tight = population and less-than optimum living conditions, the European = civilizations had to evolve, thus came technical and civilizational = advance. China, as an opposite, resolved the internal wars and became a = static society, because lack of threat caused a lack of evolution. As = Biology teaches us, evolution is never without an external factor.
eJ8+IggOAQaQCAAEAAAAAAABAAEAAQeQBgAIAAAA5AQAAAAAAADoAAEIgAcAGAAAAElQTS5NaWNy b3NvZnQgTWFpbC5Ob3RlADEIAQ2ABAACAAAAAgACAAEEkAYAtAEAAAEAAAAQAAAAAwAAMAIAAAAL AA8OAAAAAAIB/w8BAAAASwAAAAAAAACBKx+kvqMQGZ1uAN0BD1QCAAAAAGV4dHJvcGlhbnNAZXh0 cm9weS5jb20AU01UUABleHRyb3BpYW5zQGV4dHJvcHkuY29tAAAeAAIwAQAAAAUAAABTTVRQAAAA AB4AAzABAAAAFwAAAGV4dHJvcGlhbnNAZXh0cm9weS5jb20AAAMAFQwBAAAAAwD+DwYAAAAeAAEw AQAAABkAAAAnZXh0cm9waWFuc0BleHRyb3B5LmNvbScAAAAAAgELMAEAAAAcAAAAU01UUDpFWFRS T1BJQU5TQEVYVFJPUFkuQ09NAAMAADkAAAAACwBAOgEAAAAeAPZfAQAAABcAAABleHRyb3BpYW5z QGV4dHJvcHkuY29tAAACAfdfAQAAAEsAAAAAAAAAgSsfpL6jEBmdbgDdAQ9UAgAAAABleHRyb3Bp YW5zQGV4dHJvcHkuY29tAFNNVFAAZXh0cm9waWFuc0BleHRyb3B5LmNvbQAAAwD9XwEAAAADAP9f AAAAAAIB9g8BAAAABAAAAAAAAAKZYgEEgAEAQQAAAFJFOiBEaWZmZXJlbmNlcyAtIHdhcyBSZTog PkggZ2VuZGVyIGFwYXJ0aGVpZCBhbmQgdHJhbnNodW1hbmlzdHMAnBYBBYADAA4AAADOBwsAFwAK ABUAKgABAEEBASCAAwAOAAAAzgcLABcACgAFAAkAAQAQAQEJgAEAIQAAAEJDRjBGQTdGQkIxNkJF MTE4MUQ4QTY2NkNFNzk3QzIxAHAHAQOQBgAcCQAAIQAAAAsAAgABAAAACwAjAAAAAAADACYAAAAA AAsAKQAAAAAAAwAuAAAAAAADADYAAAAAAEAAOQDgx/Guwha+AR4AcAABAAAAQQAAAFJFOiBEaWZm ZXJlbmNlcyAtIHdhcyBSZTogPkggZ2VuZGVyIGFwYXJ0aGVpZCBhbmQgdHJhbnNodW1hbmlzdHMA AAAAAgFxAAEAAAAWAAAAAb4Wwq7qH2mCAoK3EdK140RFU1QAAAAAHgAeDAEAAAAFAAAAU01UUAAA AAAeAB8MAQAAABUAAABrYmFlbmRlckBiaWdmb290LmNvbQAAAAADAAYQs3dRLwMABxDMBQAAHgAI EAEAAABlAAAASUNBTlRISU5LT0ZQTEVOVFlPRkNBU0VTV0hFUkVHRU5ERVJET0VTTUFLRUFESUZG RVJFTkNFQlVUT05NQU5ZVFJBSVRTLFRIRURJRkZFUkVOQ0VTU0VFTU9WRVJSQVRFREZPUgAAAAAC AQkQAQAAANMFAADPBQAAOwgAAExaRnWlY5tzPwAKAQMB9wKkA+MCAGNowQrAc2V0MCAHEwKDhwBQ AvICAHBycTIPSdhUYWgDcQKAfQqACMjiOwlvMjU1AoAKhAswgwvjATA+IEkgYwORBHRoC4BrIG9m IPMLUAnwdHkWghXwD6AEIMR3aASQZSBnCfAEgVggZG8HkQDAaxgAYQsYgAaQZhfhbmNlLjggQnUF QAIgGNFueScKogqAFbB0cgtwdHM+LBYhGAAZWAQgD6BlbW0WgHYEkBtQdAmAGfBG4QWxZXhhbRbB G6AX0NBpZ2h0GfBNB0AHkdcKwBgAGlFhHSFhGCAaxrRzbB6ybBcQAZBsFtD/BcAWMAORGYAAwB8x GfQGkKUVwXcX4nRvFiBlIYDEIHkIYCAiWSQhA/D9I+FzEiAAICExB4APsBYgxHdvGsZwZW8WwRnw XE9uGAAEABkhbh1gaRcdIBHwD3BpGSBkdWzfBUAiQhugG1EPoGQi4AOg/yfyC2AYQBnwJ/AfkRvB BcD3Jzkaxh8QcygRJdADgSjqVkUXcAVAQQNQaRXwIvcZ8CRjCGBsKVAJAA+gGNAVAiBlFxAoH7gp IGLzD7AnoG5nIcIloRfQGsa/IkIklDCAIVUm0SqTZgDQdyOQD5AcsHceIRuzGCVibwcwIoAaxAr0 YwBBKeFn0DEwMzMaxFcX0AOg/nUAkDDRLaAnkS2gLgAbkfskEhXyZyOgAiAhMS9hF7DYYXk6AHAH QHkvIRkw/nMiQQMgCrEaMRagGTAJwO8IYDRwKfEZIHALUCFBG9H9CXBzKGEEICOSG9EXwAbw7xgB PBIuRBXxbiqAGBAKUH8EEQNSO9cjkQORC4AZUHZOaShQB0AZ8FNvKOJjzwcxG/s/RTKhanUBABgg vSlQYhdxKVAfsjnSLQIg/0VxGAAE8BfQB4A+5zmEAZB/GQIf9AQgMIEjQDeRC2By/xggO/QigSfw JzEnMT9wFrC/A2ACYBzhA/AWMBawaDqgzy4BAyAdYBtAaWIaIAeQ/xugF8AuAErgFfIyoQEAHXD8 cm0LgClBSrIIYAVASjV/GfQakSVhAjAHQBtALaBw/y8RHKEDcBgASjUigR8QTzHtSUB5D2AI8WdC ckwBBUBvUbAtoFEBH2MiCQBLUWn6egmAIhugAQAdIAkAPLD/KUEjkTKhN8BEwzmzHLAmcP1CYGYu AEj1SqQZ0AAgC3EfFeFCQU1BOGQZ8ElNT/8bshfxGJM/ch4AOGFPE0pQ/moFkCejB4BN8UTRFqBN Jv8wwguAI8IesBmyFoAFwCqE/09kFeEKsDVAIOAnoCJxNZj/CoA/Ay6zP7lJkTPhVpE8Yv9bgQMA VqBUVU9iGkIbwihA/nYAcAGQR+I8YhlQLDBkCP8XMkFxU9EnkQIgIoFgNhyh/zowMPMboESXJ6Ae wRawJpDPKGBmYzxTHzFzLSHTJpD5J6BtdRzwIOBBgDDRBaC3QVFmcxukRQhwJpBlA5H/ZfsegChA I4JUYAbwHSAbov83wBXhUIEdcA9gYoFLYjxxr2X6S2JkAhnSQxZBYRug/xdwQQJUoS8QG3Ao0geQ bpL/VOEb0VzSBKBCgTRANAE8Yv8wgG9DOuE4MlagUGBCYA+w3nloAXVhVWFIkWMWcxYw/wlwMSF3 AzyBd1duchogZoH1GfBBBCBCZoBSASFBbOD/RgEEIDfAG6B5Z0nDVGEFwP9N1gORHgB0VTO0NY0B QDaV0RCRczE3EHEtgGwaxNZrRJAYM0A1QGcCECqA+i4FoG01lTZSKGAA8GTxj0phKVAe0E/wOi8v B4A/BtAEkCKAJ7BOsAEAL37OSxdwB3AEcG8vfxMoYD8BQYPlAUAa04BvGuIiVnMIkDIhTGVL8RgQ C2B1gzCALLFkYVwnZBag/wCQTQIWYIpSI0A/YIrzhCGXHrAg4EyBaXfxIFYFsH8IcB1wAxAYACcA JDAFsGS7JwB54CKHj4EYEoEAkKAAAwAQEAEAAAADABEQAAAAAAMAgBD/////QAAHMKAF2F7AFr4B QAAIMKAF2F7AFr4BCwAAgAggBgAAAAAAwAAAAAAAAEYAAAAAA4UAAAAAAAADAAKACCAGAAAAAADA AAAAAAAARgAAAAAQhQAAAAAAAAMABYAIIAYAAAAAAMAAAAAAAABGAAAAAFKFAAC3DQAAAwAIgAgg BgAAAAAAwAAAAAAAAEYAAAAAAYUAAAAAAAAeAAqACCAGAAAAAADAAAAAAAAARgAAAABUhQAAAQAA AAQAAAA4LjAACwAMgAggBgAAAAAAwAAAAAAAAEYAAAAADoUAAAAAAAADAA2ACCAGAAAAAADAAAAA AAAARgAAAAARhQAAAAAAAAMADoAIIAYAAAAAAMAAAAAAAABGAAAAABiFAAAAAAAAHgBEgAggBgAA AAAAwAAAAAAAAEYAAAAANoUAAAEAAAABAAAAAAAAAB4ARYAIIAYAAAAAAMAAAAAAAABGAAAAADeF AAABAAAAAQAAAAAAAAAeAEaACCAGAAAAAADAAAAAAAAARgAAAAA4hQAAAQAAAAEAAAAAAAAAHgA9 AAEAAAAFAAAAUkU6IAAAAAADAA00/TcAAIeA