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   Message 95,540 of 95,770   
   Dawn Flood to Paul Aubrin   
   Re: If predictions fail your hypothesis    
   27 Jan 26 12:48:46   
   
   XPost: alt.global-warming, alt.atheism, alt.messianic   
   From: Dawn.Belle.Flood@gmail.com   
      
   On 1/27/2026 12:17 PM, Paul Aubrin wrote:   
   > Le 27/01/2026 à 14:30, Dawn Flood a écrit :   
   >> On 1/27/2026 12:16 AM, Paul Aubrin wrote:   
   >>> Le 27/01/2026 à 01:23, Dawn Flood a écrit :   
   >>>>> To day I danced a rain dance. If it rains tomorrow, how would you   
   >>>>> explain that ?   
   >>>>>   
   >>>>   
   >>>> Only if your predictions can constitute a statistically significant   
   >>>> result   
   >>>   
   >>> That is not enough. One single erroneous prediction can invalidate a   
   >>> false hypothesis. But you need many good predictions, all over the   
   >>> validity domain, to gain confidence in a new hypothesis.   
   >>> All the climate models failed the comparison with observations over   
   >>> the 1979 to 2016 periodd.   
   >>>   
   >>   
   >> They also fail over the 2016-2017 period, as well as this past   
   >> weekend. Try extending your graph instead of cropping it.   
   >   
   > 1) the comparison with reality (observation) became statistically   
   > significant in 2016.   
   > 2016-1979 = 37 years, that is more than the 30 years which define   
   > "climate".   
   > 2) A single counter-example is enough to invalidate a general hypothesis   
   > of physics.   
      
   As I posted already, here is the regression equation that I get using   
   the NASA GISS & the Mauna Loa CO2 observatory:   
      
   Regression Analysis: Temp versus CO2 (1959 -- 2025)   
      
   The regression equation is   
   Temp = - 351 + 1.08 CO2   
      
      
   Predictor     Coef  SE Coef       T      P   
   Constant   -350.57    12.77  -27.46  0.000   
   CO2        1.08012  0.03519   30.69  0.000   
      
      
   S = 9.40662   R-Sq = 93.5%   R-Sq(adj) = 93.4%   
      
      
   Analysis of Variance   
      
   Source          DF     SS     MS       F      P   
   Regression       1  83344  83344  941.91  0.000   
   Residual Error  65   5751     88   
   Total           66  89096   
      
      
   Unusual Observations   
      
   Obs  CO2    Temp     Fit  SE Fit  Residual  St Resid   
     66  425  128.00  108.06    2.51     19.94      2.20R   
      
   R denotes an observation with a large standardized residual.   
      
   END OUTPUT   
      
   Ditch all the climate models if you wish, and run the regression for   
   yourself.   
      
   Dawn   
      
   --- SoupGate-Win32 v1.05   
    * Origin: you cannot sedate... all the things you hate (1:229/2)   

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