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Mathematics Of Correctness : When To Give Up ?


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Consider an outcome based on 5 sequential decisions :

N=5 (mandatory)

 

Probability of Success P(x) >= 0.5 / step

 

0.5, 0.5, 0.5, 0.5, 0.5 Total = 2.50 Person A

0.4, 0.3, 0.4, 0.8, 0.7 Total = 2.60 Person B

 

Person B takes a hit initially, but gains in the long term.

 

0.1, 0.1, 0.1, x, y Total = NA Person C

 

Person C should give up after event 3 because he cannot reach 2.5 (0.5 * 5), even if he perfects outcome 4 and 5. (1/1).

 

Is giving up not as stupid as it seems ?  :bow:  :bow: 

 

P.S. Application could be a sequential manufacturing industry like diamond processing.

 

Steps are -

 

  • Cutting
  • Polishing
  • Interim QA  - introduce a micro QA for minimizing time of subsequent steps for non-eligible diamonds 
  • Bagging
  • Setting
  • QA
Edited by petrushkagoogol
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You may wish to look up "combined probabilities" and "Bayes' Theorem"... 

 

It is, however, true that people rarely know when to "give up" when they really should.

 

 

The 50-50-90 rule: anytime you have a 50-50 chance of getting something right, there's a 90% probability you'll get it wrong, :phones:

Buffy

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  • 4 weeks later...

 

 

It is, however, true that people rarely know when to "give up" when they really should.

 

 

Is this why gambling is addictive to some?

 

If the weather forecast calls for a 20% chance of showers, it seems there is a 90% chance it will rain on the hay field that is almost ready to bale.

 

I don't need to gamble because I farm.

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  • 2 weeks later...

Is this why gambling is addictive to some?

 

If the weather forecast calls for a 20% chance of showers, it seems there is a 90% chance it will rain on the hay field that is almost ready to bale.

 

I don't need to gamble because I farm.

Just seen this and I am sure it is all too true. Though I suspect we all tend to remember disproportionately those occasions on which a forecast was wrong, rather than the more routine incidences of them being right.

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