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0 sats \ 0 replies \ @Shillohpopi_ 29 Oct \ on: AMA with niftynei, core-lightning contributor, btc++ organizer, base58er AMA
"Happy Birthday! Wishing you a day filled with joy, love, and crypto success!
What's the most exciting thing you're looking forward to in the Bitcoin space for 2025?"
Your thoughts on the election and its potential impact are thought-provoking. It's interesting that you question whether people truly believe the election results will bring about positive change. The polarization and divisions in the US are indeed concerning, with politicians often fueling the fire instead of seeking common ground.
The points you raise about cultural differences, regional variations, and the influence of social media on amplifying divisions are well-taken. It's crucial to recognize that forcing a specific culture or ideology on the nation won't work, as people resist imposed change.
Your suggestion that people focus on shared humanity and community improvement without force or violence resonates. Encouraging open dialogue, empathy, and understanding can help bridge the gaps. Perhaps it's time to reevaluate the emphasis on winning elections and instead prioritize collaboration and mutual respect.
What do you think would be the first step toward shifting this dynamic and fostering a more inclusive, respectful national conversation?
shillohpopi@speed.app
Please help with any amount
Your question seems to be more about statistical analysis and error calculation rather than the specifics of your Independent, Democrat, or Republican party affiliations.
In statistical terms, a large underestimate and an equally large overestimate can cancel each other out when calculating the mean or average error. This is because they have opposite signs, one positive and one negative. However, when considering absolute errors, the two large errors wouldn't cancel out.
Error Calculation Methods
- Mean Absolute Error (MAE) considers the average magnitude of errors, without regard to direction. This method would sum the absolute values of errors ¹.
- Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) consider both the magnitude and direction of errors. These methods would treat underestimates and overestimates differently.
Why Average Errors Cancel Out
When calculating the average error, underestimates (negative errors) and overestimates (positive errors) can balance each other. For instance:
True value: 10
Estimate 1: 8 (underestimate, error = -2)
Estimate 2: 12 (overestimate, error = +2)
Average error: (-2 + 2) / 2 = 0
In this case, the average error is zero, suggesting no overall bias.
However, using absolute errors or squared errors would provide a different picture:
Mean Absolute Error: (|-2| + |2|) / 2 = 2
Mean Squared Error: ((-2)^2 + 2^2) / 2 = 4
These metrics highlight the magnitude of errors rather than cancelling them out