Media is too big
VIEW IN TELEGRAM
Trump: They're giving us a free jet ... when they give you a putt, you pick it up and you walk to the next hole
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
😁4🗿4🔥2😐1😡1
NOW: Pumpfun launches creator rewards
Is this a way to try to incentivize coin creators not to hard dump and wreck the coins?
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
Is this a way to try to incentivize coin creators not to hard dump and wreck the coins?
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
👀6
NEW: SEC Chair Paul Atkins calls it a “new day” at the agency, ending enforcement-first tactics and pledging fit-for-purpose crypto rules
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
👀7👏4🙏1
DoomPosting
But what about my mixed mut who outperforms these other breeds? Glad you asked Enter admixture studies Shockingly huge linear correlation between percent of ancestors of a given race -vs- attributes associated with that race I.e. proportion on basic traits…
Bro in the chat trying to say this chart shows nothing
…because of some kind of r-value retardation
— Well, use of r-values, in any way other than totally ignoring them, is always retarded
Why? Because r-values implicitly focus on the average / 50th percentile — when, in reality, due to markets and other factors, average is virtually NEVER what’s most important at all
Rather EXTREMES, NOT AVERAGES, are what matters,
And so everything not at the extremes should be completely ignored for practical purposes, virtually always in reality,
e.g.:
• Tiny top N% of top performers of of the whole population, in any given field, are the only ones from the population that are doing the challenging jobs in that field
• Top 1% of criminals do more than 50% of the violent crime
• Tiny top N% of super hoes are doing most of the hoeing
etc
— EXTREMES of the distribution curves are all that ever matters in practice, nearly always
ALWAYS be aware of what part(s) of the distribution your statistical measures are implicitly focusing on, and if it’s the average, ignore that BS
— This focusing on averages, when averages are irrelevant, is one of the most common lying-with-statistics scams
This is the same kind of lying-with-statistics nonsense as the left uses when trying to say that men and women should have equal outcomes on all jobs, since the AVERAGE distribution of the greater male variability hypothesis curves are nearly the same — despite the EXTREMES of the curves being vastly different, due to the men’s curve’s tails being much fatter than the womens.
Averages of two distribution curves are nearly the same? — IRRELEVANT
Extremes are virtually all that ever matters in practice, never averages
Now, at those 2 circles highlighting the upper extremes on the chart above, and try to tell me that this chart shows nothing
Always focus on extremes, never averages, because the extremes drive the world
(You could attack other things, in theory, but saying the chart shows nothing because of r-values is nonsensical BS)
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
…because of some kind of r-value retardation
— Well, use of r-values, in any way other than totally ignoring them, is always retarded
Why? Because r-values implicitly focus on the average / 50th percentile — when, in reality, due to markets and other factors, average is virtually NEVER what’s most important at all
Rather EXTREMES, NOT AVERAGES, are what matters,
And so everything not at the extremes should be completely ignored for practical purposes, virtually always in reality,
e.g.:
• Tiny top N% of top performers of of the whole population, in any given field, are the only ones from the population that are doing the challenging jobs in that field
• Top 1% of criminals do more than 50% of the violent crime
• Tiny top N% of super hoes are doing most of the hoeing
etc
— EXTREMES of the distribution curves are all that ever matters in practice, nearly always
ALWAYS be aware of what part(s) of the distribution your statistical measures are implicitly focusing on, and if it’s the average, ignore that BS
— This focusing on averages, when averages are irrelevant, is one of the most common lying-with-statistics scams
This is the same kind of lying-with-statistics nonsense as the left uses when trying to say that men and women should have equal outcomes on all jobs, since the AVERAGE distribution of the greater male variability hypothesis curves are nearly the same — despite the EXTREMES of the curves being vastly different, due to the men’s curve’s tails being much fatter than the womens.
Averages of two distribution curves are nearly the same? — IRRELEVANT
Extremes are virtually all that ever matters in practice, never averages
Now, at those 2 circles highlighting the upper extremes on the chart above, and try to tell me that this chart shows nothing
Always focus on extremes, never averages, because the extremes drive the world
(You could attack other things, in theory, but saying the chart shows nothing because of r-values is nonsensical BS)
🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
💯4🤯2👀1